Robert E. Schapire, Peter Stone, David Mcallester, Michael L. Littman, János A. Csirik
In complicated, interacting auctions, a fundamental problem is the prediction of prices of goods in the auctions, and more broadly, the modeling of uncertainty regarding these prices. In this paper,...
Patrick Beeson, Peter Stone, Associate Professor, Benjamin Kuipers Professor, Gregory Dudek Professor, Paul Newman, ...
A General Purpose Task Specification Language for Bootstrap Learning (2009)
Ian Fasel, Michael Quinlan, Peter Stone
Reinforcement learning (RL) is an effective framework for online learning by autonomous agents. Most RL research focuses on domain-independent learning algorithms, requiring an expert human to define...
USING NEUROEVOLUTION APPROVED BY SUPERVISING COMMITTEE: (2009)
Aravind Gowrisankar, Risto Miikkulainen Supervisor, Peter Stone, To Amma
I would like to thank Risto Miikkulainen for his patient support during all stages of this thesis. Risto’s Neural Network class inspired me to start doing research in neuroevolution. Thanks to Ugo...
Brian StankiewiczReinforcement Learning In High-Diameter, Continuous Environments (2009)
Jefferson Provost, Jefferson Provost, Benjamin J. Kuipers, Risto Miikkulainen Supervisor, Raymond Mooney, Bruce Porter, ...
A couple of years ago someone forwarded me a link to an article interpreting Frodo’s quest in The Lord of the Rings as an allegory for writing and filing a dissertation (Lee, 2005). At the time, I...
TAMER: Training an Agent Manually via Evaluative Reinforcement (2009)
Abstract—Though computers have surpassed humans at many tasks, especially computationally intensive ones, there are many tasks for which human expertise remains necessary and/or useful. For such...
Hierarchical Model-Based Reinforcement Learning: R-MAX + MAXQ (2009)
Hierarchical decomposition promises to help scale reinforcement learning algorithms naturally to real-world problems by exploiting their underlying structure. Model-based algorithms, which provided...
Domestic Interaction on a Segway Base (2009)
W. Bradley Knox, Juhyun Lee, Peter Stone
Abstract. To be useful in a home environment, an assistive robot needs to be capable of a broad range of interactive activities such as locating objects, following specific people, and distinguishing...
Online Kernel Selection for Bayesian Reinforcement Learning (2009)
Joseph Reisinger, Peter Stone, Risto Miikkulainen
Kernel-based Bayesian methods for Reinforcement Learning (RL) such as Gaussian Process Temporal Difference (GPTD) are particularly promising because they rigorously treat uncertainty in the value...
Online Multiagent Learning against Memory Bounded Adversaries (2009)
Doran Chakraborty, Peter Stone
Abstract. The traditional agenda in Multiagent Learning (MAL) has been to develop learners that guarantee convergence to an equilibrium in self-play or that converge to playing the best response...
CARVE: A Cognitive Agent for Resource Value Estimation (2009)
Jonathan Wildstrom, Peter Stone, Computer Sciences
Recent years have seen a growing push toward utilitycomputing [10, 6]. In this new paradigm, computation and memory are treated as a utility similar to electricity or wa-ter, and "service...
Multiagent Interactions in Urban Driving (2009)
Patrick Beeson, Bartley Gillan, Tarun Nimmagadda, Mickey Ristroph, David Li, Peter Stone
between fully autonomous vehicles. Unlike previous challenges, the Urban Challenge vehicles had to follow the California laws for driving, including properly handling traffic. This article presents...
Hierarchical Model-Based Reinforcement Learning: R-MAX + MAXQ (2009)
Hierarchical decomposition promises to help scale reinforcement learning algorithms naturally to real-world problems by exploiting their underlying structure. Model-based algorithms, which provided...
A Multiagent Approach to Autonomous Intersection Management (2009)
Artificial intelligence research is ushering in a new era of sophisticated, mass-market transportation technology. While computers can already fly a passenger jet better than a trained human pilot,...
Adaptive Tile Coding for Reinforcement Learning (2009)
Shimon Whiteson, Matthew E. Taylor, Peter Stone
• Most real-world reinforcement learning tasks have large or continuous state spaces • Table-based approaches are infeasible, need function approximators (FAs) instead • Many types of function...
ABSTRACT Autonomous Transfer for Reinforcement Learning (2009)
Matthew E. Taylor, Gregory Kuhlmann, Peter Stone
Recent work in transfer learning has succeeded in making reinforcement learning algorithms more efficient by incorporating knowledge from previous tasks. However, such methods typically must be...
Coach: A Machine Learning Approach (2009)
In Daniele Nardi, Martin Riedmiller, Claude Sammut, Soccer World, Cup Viii, ...
Abstract. The UT Austin Villa 2003 simulated online soccer coach was a first time entry in the RoboCup Coach Competition. In developing the coach, the main research focus was placed on treating...
• Limited vision � Limited communication (2009)
Peter Stone, Richard S. Sutton, Gregory Kuhlmann, Presented Maysam Heydari
� Challenges posed by RoboCup Soccer: � fully distributed, multi-agent domain with teammates and adversaries � Large state space
ABSTRACT Autonomous Transfer for Reinforcement Learning (2008)
Matthew E. Taylor, Gregory Kuhlmann, Peter Stone
Recent work in transfer learning has succeeded in making reinforcement learning algorithms more efficient by incorporating knowledge from previous tasks. However, such methods typically must be...
Instance-Based Action Models for Fast Action (2008)
Planning Mazda Ahmadi, Peter Stone
Abstract. Two main challenges of robot action planning in real domains are uncertain action effects and dynamic environments. In this paper, an instance-based action model is learned empirically by...
A RESERVATION-BASED MULTIAGENT SYSTEM FOR INTERSECTION CONTROL (2008)
Abstract: Traffic congestion is one of the leading causes of lost productivity and decreased standard of living in urban settings. Recent advances in artificial intelligence suggest that autonomous...
Long-term vs. Greedy Action Planning for Color Learning on a Mobile Robot (2008)
Abstract: A major challenge to the deployment of mobile robots is the ability to function autonomously, learning appropriate models for environmental features and adapting those models in response to...
Peter Stone, Daniel Lee, Benjamin Kuipers, Manuela Veloso
Additional references are available upon request.
In Daniel Polani, Brett Browning, Andrea Bonarini, Kazuo Yoshida, Soccer World, ...
Abstract. The UT Austin Villa 2003 simulated online soccer coach was a first time entry in the RoboCup Coach Competition. In developing the coach, the main research focus was placed on treating...
Springer Verlag, 2008. Model-based Reinforcement Learning in a Complex Domain (2008)
Shivaram Kalyanakrishnan, Peter Stone, Yaxin Liu
Abstract. Reinforcement learning is a paradigm under which an agent seeks to improve its policy by making learning updates based on the experiences it gathers through interaction with the...
ÓÒÐÝ Û��Ò Ð���Ð� � ØÖ��Ò�Ò � ��Ø � � × �Ú��Ð��Ð � Ì�� × Ô�Ô�Ö (2008)
Shimon Whiteson, Risto Miikkulainen, Peter Stone, Nate Kohl, Kenneth O. Stanley
ÔÖ�×�ÒØ × � ÒÓÚ�Ð Ñ�Ø�Ó � �ÐÐ� � �Ë Æ��Ì Û� � � �ÜØ�Ò� × Ø�� Æ��Ì Ò�ÙÖÓ�ÚÓÐÙØ�ÓÒ Ñ�Ø�Ó � ØÓ...
David Pardoe, Peter Stone, Mark Vanmiddlesworth
Abstract. Supply chains are ubiquitous in the manufacturing of many complex products. Traditionally, supply chains have been created through the interactions of human representatives of the companies...
Charles Lee Isbell, Jr. Michael Kearns, Satinder Singh, Christian R. Shelton, Peter Stone, Dave Kormann, ...
Abstract We describe our development of Cobot, a novel software agent who lives in LambdaMOO, a popular virtual world frequented by hundreds of users. Cobot’s goal was to become an actual part of...
ABSTRACT Transfer via Inter-Task Mappings in Policy Search Reinforcement Learning (2008)
Matthew E. Taylor, Shimon Whiteson, Peter Stone
The ambitious goal of transfer learning is to accelerate learning on a target task after training on a different, but related, source task. While many past transfer methods have focused on...
Representation Transfer via Elaboration (2008)
Matthew E. Taylor, Peter Stone
A key component of any reinforcement learning (RL) algorithm is the underlying representation used by the agent for learning (e.g. the parameterization of its function approximator). Transfer...
Shivaram Kalyanakrishnan, Yaxin Liu, Peter Stone
Abstract. We present half field offense, a novel subtask of RoboCup simulated soccer, and pose it as a problem for reinforcement learning. In this task, an offense team attempts to outplay a defense...
Reinforcement Learning for RoboCup Soccer (2008)
Peter Stone, Richard S. Sutton, Gregory Kuhlmann, Peter Stone, Richard S. Sutton, Gregory Kuhlmann
On behalf of:
Accelerating Search with Transferred Heuristics (2008)
Matthew E. Taylor, Gregory Kuhlmann, Peter Stone
A common goal for transfer learning research is to show that a learner can solve a source task and then leverage the learned knowledge to solve a target task faster than if it had learned the target...
Three Automated Stock-Trading Agents:A Comparative Study (2008)
1 Introduction Automated stock trading is a burgeoning research area with important practical applica-tions. The advent of the Internet has radically transformed the nature of stock trading in most...
Douglas Aberdeen, Owen Thomas, Oliver Buffet, Drago Bokal, Gasper Fijavz, Sergio De Florio, ...
10 22 26 28 32 36
Comparing Two Action Planning Approaches for Color Learning on a Mobile Robot (2008)
Abstract. A major challenge to the deployment of mobile robots in a wide range of tasks is the ability to function autonomously, learning appropriate models for environmental features and adapting...
Towards Eliminating Manual Color Calibration (2008)
In Itsuki Noda, Adam Jacoff, Ansgar Bredenfeld, Yasutake Takahashi, At Robocup, Mohan Sridharan, ...
Abstract. Color calibration is a time-consuming, and therefore costly requirement for most robot teams at RoboCup. This paper presents an approach for autonomous color learning on-board a mobile...
Springer Verlag, 2008. A Neural Network-Based Approach to Robot Motion Control (2008)
Uli Grasemann, Daniel Stronger, Peter Stone
Abstract. The joint controllers used in robots like the Sony Aibo are designed for the task of moving the joints of the robot to a given position. However, they are not well suited to the problem of...
4.0. Thesis: Robust Autonomous Structure-Based Color Learning on a Mobile Robot. (2008)
Mohan Sridharan, Caroline Road, Advisors Dr, Peter Stone, Dr. Benjamin Kuipers
www.cs.bham.ac.uk/∼mzs
Expectation-Based Vision for Self-Localization on a Legged Robot (2008)
This paper presents and empirically compares solutions to the problem of vision and self-localization on a legged robot. Specifically, given a series of visual images produced by a camera on-board...
Evolving Soccer Keepaway Players through Task Decomposition (2008)
Shimon Whiteson, Nate Kohl, Risto Miikkulainen, Peter Stone
Abstract. Complex control tasks can often be solved by decomposing them into hierarchies of manageable subtasks. Such decompositions require designers to decide how much human knowledge should be...
Polynomial Regression with Automated Degree: A Function Approximator for Autonomous Agents (2008)
Abstract. In order for an autonomous agent to behave robustly in a variety of environments, it must have the ability to learn approximations to many different functions. The function approximator...
Autonomous Planned Color Learning on a Legged Robot (2008)
Abstract. Our research focuses on automating the color-learning process on-board a legged robot with limited computational and memory resources. A key defining feature of our approach is that instead...
Dave Tuttle, Dave Tuttle, Prof Peter Stone, Peter Stone, Patrick Beeson, Tekin Meriçli, ...
not guarantee the accuracy or reliability of the information in this paper."
Charles Lee Isbell, Jr. Michael Kearns, Satinder Singh, Christian R. Shelton, Peter Stone, Dave Kormann, ...
Abstract We describe our development of Cobot, a novel software agent who lives in LambdaMOO, a popular virtual world frequented by hundreds of users. Cobot’s goal was to become an actual part of...
Empirical Studies in Action Selection with Reinforcement Learning (2008)
Shimon Whiteson, Matthew E. Taylor, Peter Stone, Shimon Whiteson, Matthew E. Taylor, Peter Stone
On behalf of:
Layered Learning towards Autonomic Computing (2008)
Layered learning [2] is a general-purpose machine learning paradigm for complex domains in which learning a mapping directly from agents ’ sensors to their actuators is intractable. Given a...
Adaptive Tile Coding for Value Function Approximation (2008)
Shimon Whiteson, Matthew E. Taylor, Peter Stone
Reinforcement learning problems are commonly tackled by estimating the optimal value function. In many real-world problems, learning this value function requires a function approximator, which maps...
Abstract. Color segmentation is a challenging yet integral subtask of mobile robot systems that use visual sensors, especially since such systems typically have limited computational and memory...
The Trading Agent Competition Supply Chain Management Scenario (TAC SCM) [Sadeh (2008)
This paper introduces TacTex-03, an agent designed to participate in the Trading Agent Competition Supply Chain Management Scenario (TAC SCM). As specified by this scenario, TacTex-03 acts as a...
Shivaram Kalyanakrishnan, Yaxin Liu, Peter Stone
Abstract. We present half field offense, a novel subtask of RoboCup simulated soccer, and pose it as a problem for reinforcement learning. In this task, an offense team attempts to outplay a defense...
Three Automated Stock-Trading Agents:A Comparative Study (2008)
1 Introduction Automated stock trading is a burgeoning research area with important practical applica-tions. The advent of the Internet has radically transformed the nature of stock trading in most...
Transfer Learning and Intelligence: an Argument and Approach (2008)
Matthew E. Taylor, Gregory Kuhlmann, Peter Stone
Abstract. In order to claim fully general intelligence in an autonomous agent, the ability to learn is one of the most central capabilities. Classical machine learning techniques have had many...
Springer Verlag, 2007. Selective Visual Attention for Object Detection on a Legged Robot (2008)
Abstract. Autonomous robots can use a variety of sensors, such as sonar, laser range finders, and bump sensors, to sense their environments. Visual information from an onboard camera can provide...
Adaptive Tile Coding for Value Function Approximation (2008)
Shimon Whiteson, Matthew E. Taylor, Peter Stone
Reinforcement learning problems are commonly tackled by estimating the optimal value function. In many real-world problems, learning this value function requires a function approximator, which maps...
Matthew E. Taylor, Shimon Whiteson, Peter Stone
Reinforcement learning (RL) methods have become popular in recent years because of their ability to solve complex tasks with minimal feedback. Both genetic algorithms (GAs) and temporal difference...
ABSTRACT Automatic Feature Selection in Neuroevolution (2008)
Shimon Whiteson, Risto Miikkulainen, Peter Stone, Nate Kohl
Feature selection is the process of finding the set of inputs to a machine learning algorithm that will yield the best performance. Developing a way to solve this problem automatically would make...
Coach: A Machine Learning Approach (2008)
In Daniele Nardi, Martin Riedmiller, Claude Sammut, Soccer World, Cup Viii, ...
Abstract. The UT Austin Villa 2003 simulated online soccer coach was a first time entry in the RoboCup Coach Competition. In developing the coach, the main research focus was placed on treating...
ABSTRACT Transfer via InterTask Mappings in Policy Search Reinforcement Learning (2008)
Matthew E. Taylor, Shimon Whiteson, Peter Stone
The ambitious goal of transfer learning is to accelerate learning on a target task after training on a different, but related, source task. While many past transfer methods have focused on...
An Autonomous Agent for Supply Chain Management Abstract (2008)
Supply Chain Management involves planning for the procurement of materials, assembly of finished products from these materials, and distribution of products to customers. The Trading Agent...
Transfer learning via inter-task mappings for temporal difference learning (2008)
Matthew E. Taylor, Peter Stone, Yaxin Liu, L. Littman
Temporal difference (TD) learning (Sutton and Barto, 1998) has become a popular reinforcement learning technique in recent years. TD methods, relying on function approximators to generalize learning...
Evolving Soccer Keepaway Players through Task Decomposition (2008)
Shimon Whiteson, Nate Kohl, Risto Miikkulainen, Peter Stone
Abstract. Complex control tasks can often be solved by decomposing them into hierarchies of manageable subtasks. Such decompositions require designers to decide how much human knowledge should be...
1 A Distributed Biconnectivity Check (2008)
Summary. For many distributed autonomous robotic systems, it is important to maintain communication connectivity among the robots. That is, each robot must be able to communicate with each other...
Abstract. This paper presents layered learning, a hierarchical machine learning paradigm. Layered learning applies to tasks for which learning a direct mapping from inputs to outputs is intractable...
Accelerating Search with Transferred Heuristics (2008)
Matthew E. Taylor, Gregory Kuhlmann, Peter Stone
A common goal for transfer learning research is to show that a learner can solve a source task and then leverage the learned knowledge to solve a target task faster than if it had learned the target...
Evolving Soccer Keepaway Players through Task Decomposition (2008)
Shimon Whiteson, Nate Kohl, Risto Miikkulainen, Peter Stone
Abstract. Complex control tasks can often be solved by decomposing them into hierarchies of manageable subtasks. Such decompositions require designers to decide how much human knowledge should be...
ABSTRACT IFSA: Incremental Feature-Set Augmentation for Reinforcement Learning Tasks (2008)
Mazda Ahmadi, Matthew E. Taylor, Peter Stone
Reinforcement learning is a popular and successful framework for many agent-related problems because only limited environmental feedback is necessary for learning. While many algorithms exist to...
The Trading Agent Competition Supply Chain Management Scenario (TAC SCM) [Sadeh (2008)
This paper introduces TacTex-03, an agent designed to participate in the Trading Agent Competition Supply Chain Management Scenario (TAC SCM). As specified by this scenario, TacTex-03 acts as a...
Matthew E. Taylor, Shimon Whiteson, Peter Stone
Reinforcement learning (RL) methods have become popular in recent years because of their ability to solve complex tasks with minimal feedback. Both genetic algorithms (GAs) and temporal difference...
Springer Verlag, 2008. Instance-Based Action Models for Fast Action Planning (2008)
Abstract. Two main challenges of robot action planning in real domains are uncertain action effects and dynamic environments. In this paper, an instance-based action model is learned empirically by...
: /www.ep.liu.se/ea/cis/1999/007/02/ The CMUnited-99 Simulator Team (2008)
Peter Stone, Manuela Veloso, Patrick Riley
Abstract. The CMUnited-98 simulator team became the 1998 RoboCup simulator league champion by winning all 8 of its games, outscoring opponents by a total of 66–0. CMUnited-99 builds upon the...
Abstract. Reinforcement learning (RL) is a powerful abstraction of sequential decision making that has an established theoretical foundation and has proven effective in a variety of small, simulated...
An Autonomous Agent for Supply Chain Management Abstract (2008)
Supply Chain Management involves planning for the procurement of materials, assembly of finished products from these materials, and distribution of products to customers. The Trading Agent...
A Neural Network-Based Approach to Robot Motion Control (2008)
In Visser, Springer Verlag, Uli Grasemann, Daniel Stronger, Peter Stone
Abstract. The joint controllers used in robots like the Sony Aibo are designed for the task of moving the joints of the robot to a given position. However, they are not well suited to the problem of...
Defining and Using Ideal Teammate andOpponent Agent Models (2008)
Peter Stone, Patrick Riley, Manuela Veloso
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Transfer Learning and Intelligence: an Argument and Approach (2008)
Matthew E. Taylor, Gregory Kuhlmann, Peter Stone
Abstract. In order to claim fully general intelligence in an autonomous agent, the ability to learn is one of the most central capabilities. Classical machine learning techniques have had many...
Detecting Motion in the World with a Moving Quadruped Robot (2008)
Peggy Fidelman, Thayne Coffman, Risto Miikkulainen, Peter Stone
Abstract. For a robot in a dynamic environment, the ability to detect motion is crucial. Although legs are arguably the most versatile means of locomotion for a robot, and thus the best suited to an...
Transfer learning via inter-task mappings for temporal difference learning (2008)
Matthew E. Taylor, Peter Stone, Yaxin Liu, L. Littman
Temporal difference (TD) learning (Sutton and Barto, 1998) has become a popular reinforcement learning technique in recent years. TD methods, relying on function approximators to generalize learning...
A Library of General-Purpose Action Descriptions Committee: (2008)
Selim Turhan Erdo˘gan, Vladimir Lifschitz Supervisor, Michael Gelfond, Benjamin J. Kuipers, Bruce Porter, Peter Stone
To my family
Michael David Bond, Kathryn S. Mckinley, Stephen M. Blackburn, Keshav Pingali, Peter Stone, Emmett Witchel, ...
I am deeply grateful to Kathryn McKinley for supporting and mentor-ing me in many ways. Kathryn has provided invaluable technical expertise and feedback even as we worked in new areas. She has been...
Applications of Probabilistic Analysis and Game Theory (2008)
Nedialko Boyanov Dimitrov, C. Greg Plaxton, Adam Klivans, David Morton, Peter Stone, David Zuckerman, ...
To my family, friends and mentors: Thank you. – Ned.
Transferring instances for model-based reinforcement learning (2008)
Matthew E. Taylor, Nicholas K. Jong, Peter Stone
Reinforcement learning agents typically require a significant amount of data before performing well on complex tasks. Transfer learning methods have made progress reducing sample complexity, but they...
Michael David Bond, Kathryn S. Mckinley, Stephen M. Blackburn, Keshav Pingali, Peter Stone, Emmett Witchel, ...
I am deeply grateful to Kathryn McKinley for supporting and mentor-ing me in many ways. Kathryn has provided invaluable technical expertise and feedback even as we worked in new areas. She has been...
Model-based reinforcement learning in a complex domain (2008)
Shivaram Kalyanakrishnan, Peter Stone, Yaxin Liu
Abstract. Reinforcement learning is a paradigm under which an agent seeks to improve its policy by making learning updates based on the experiences it gathers through interaction with the...
Diagnosing and Tolerating Bugs in Deployed Systems Committee: (2008)
Michael David Bond, Michael David Bond, Kathryn S. Mckinley, Stephen M. Blackburn, Keshav Pingali, Peter Stone, ...
I am deeply grateful to Kathryn McKinley for supporting and mentor-ing me in many ways. Kathryn has provided invaluable technical expertise and feedback even as we worked in new areas. She has been...
UT Austin Villa 2008: Standing On Two Legs (2008)
Todd Hester, Michael Quinlan, Peter Stone
In 2008, UT Austin Villa entered a team in the first Nao competition of the Standard Platform League of the RoboCup competition. The team had previous experience in RoboCup in the Aibo leagues. Using...
Diagnosing and Tolerating Bugs in Deployed Systems Committee: (2008)
Michael David Bond, Kathryn S. Mckinley, Stephen M. Blackburn, Keshav Pingali, Peter Stone, Emmett Witchel, ...
Davis, Justin Brickell, and Matt Taylor have been good friends and colleagues without the good sense to be systems researchers. This thesis would have not have been possible without Jikes RVM. I am...
Transferring instances for model-based reinforcement learning (2008)
Matthew E. Taylor, Nicholas K. Jong, Peter Stone
Abstract. Reinforcement learning agents typically require a significant amount of data before performing well on complex tasks. Transfer learning methods have made progress reducing sample...
UT Austin Villa 2008: Standing On Two Legs (2008)
Todd Hester, Michael Quinlan, Peter Stone
In 2008, UT Austin Villa entered a team in the first Nao competition of the Standard Platform League of the RoboCup competition. The team had previous experience in RoboCup in the Aibo leagues. Using...
Initial Results in RoboCup Soccer (2007)
Gregory Kuhlmann, Peter Stone, Raymond Mooney, Jude Shavlik
We describe our current efforts towards creating a reinforcement learner that learns both from reinforcements provided by its environment and from human-generated advice. Our research involves two...
Autonomous Bidding Agents Edited by (2007)
This article summarizes the bidding algorithms developed for the on-line Trading Agent Competition held in July, 2000 in Boston. At its heart, the article describes 12 of the 22 agent strategies in...
The RoboCup Synthetic Agent (2007)
Hiroaki Kitano, Manuela Veloso, Hitoshi Matsubara, Millrid Tambe, Ilvia Coradeschi, Itsuki Noda, ...
asadaraech. eng. osaka-u. ac. jp RoboCup Chollenge offers o set of chollenges for intelligent ogent reseorchers using o friendly competition in o dynomic, reol-time, multiogent domoin. While RoboCup...
Team- Part it ioned, Opaque-Transit ion Reinforcement Learning * (2007)
Abstract. We present a novel multi-agent learning paradigm called team-partitioned, opaque-transition reinforcement learning (TPOT-RL). TPOT-RL introduces the use of action-dependent features to...
Beatin a Defender in Robotic Soccer: Memory-Based Learnin of a Continuous Function (2007)
Learning how to adjust to an opponent's position is critical to the success of having intelligent agents collaborating towards the achievement of specific tasks in unfriendly environments. This...
In RoboCup-97: The First Robot World Cup Soccer Games and Conferences, (2007)
Manuela Veloso, Peter Stone, Kwun Han, Sorin Achim
Abstract. Robotic soccer is a challenging research domain which involves multiple agents that need to collaborate in an adversarial environment to achieve specific objectives. In this paper, we...
In RoboCup-97: Robot Soccer World Cup [, (2007)
Abstract. The Soccer Server system provides a rich and cha]]enging multiagent, real-time domain. Agents must accurately perceive and act despite a quickly changing, largely hidden, noisy world. They...
Elizabeth Sklar, Simon Parsons, Peter Stone
Abstract. Since team-based projects have been proven to be an effective pedagogical tool, we have been using RoboCup challenges as the basis for class projects in undergraduate courses. This paper...
Robert E. Schapire, Peter Stone, David Mcallester, Michael L. Littman
In complicated, interacting auctions, a fundamental problem is the prediction of prices of goods in the auctions, and more broadly, the modeling of uncertainty regarding these prices. In this paper,...
Conference submission (1/22/02). ATTac-2001: A Learning, Autonomous Bidding Agent (2007)
Peter Stone, Robert E. Schapire, Michael L. Littman, David Mcallester
This paper presents a general approach to building autonomous bidding agents to bid in multiple simultaneous auctions for interacting goods. The core of our approach is learning a model of the...
Peter Stone, Robert E. Schapire, Janos A. Csirik, Michael L. Littman, David Mcallester
Abstract. Auctions are becoming an increasingly popular method for transacting business, especially over the Internet. This paper presents a general approach to building autonomous bidding agents to...
Peter Stone, Michael L. Littman
Abstract. Auctions are an area of great academic and commercial interest, from tiny auctions for toys on eBay to multi-billion-dollar auctions held by governments for resources or contracts. Although...
Autono.mous nts the nt Competition (2007)
Designing agents that can bid in online simultaneous auctions is a complex task. The authors describe task-specific details and strategies of agents in a trading agent competition.
Peter Stone, Richard S. Sutton
RoboCup simulated soccer presents many challenges to reinforcement learning methods, including a large state space, hidden and uncertain state, multiple agents, and long and variable delays in the...
Janos A. Csirik, Michael L. Littman, Satinder Singh, Peter Stone
Abstract. We introduce FAucS, a software testbed for studying automated agent bidding strategies in simulated auctions, specically the United States FCC wireless frequency spectrum auctions. In...
ATTUnited-2001: Using Heterogeneous Players (2007)
ATTUnited-2001 is a successor of the CMUnited teams: CMUnited-97, CMUnited98, CMUnited-99, and ATT-CMUnited-2000. It is built mainly upon CMUnited99 [4]. It also incorporates the team action...
The CMUnited-99 Simulator Team (2007)
Peter Stone, Manuela Veloso, Patrick Riley
Abstract. The CMUnited-98 simulator team became the 1998 RoboCup simulator league champion by winning all 8 of its games, outscoring opponents by a total of 66--0. CMUnited-99 builds upon the...
Peter Stone, Patrick Riley, Manuela Veloso
A common challenge for agents in multiagent systems is trying to predict what other agents are going to do in the future. Such knowledge can help an agent determine which of its current action...
Elizabeth Sklar, Simon Parsons, Peter Stone
Abstract. Since team-based projects have been proven to be an effective pedagogical tool, we have been using RoboCup challenges as the basis for class projects in undergraduate courses. This paper...
Learning to Identify Irrelevant State Variables (2007)
When they are available, safe state abstractions improve the efficiency of reinforcement learning algorithms by allowing an agent to ignore irrelevant distinctions between states while still learning...
Towards employing PSRs in a continuous domain (2007)
Predictive State Representations (PSRs) recently emerged as an alternative framework for reasoning about stochastic environments. However, unlike Markov decision processes, they have not yet been...
Towards Autonomic Computing: Adaptive Network Routing and Scheduling (2007)
Shimon Whiteson And, Shimon Whiteson, Peter Stone
Computer systems are rapidly becoming so complex that maintaining them with human support staffs will be prohibitively expensive and inefficient. In response, visionaries have begun proposing that...
Autonomous Bidding Agents Edited by (2007)
This article summarizes the bidding algorithms developed for the on-line Trading Agent Competition held in July, 2000 in Boston. At its heart, the article describes 12 of the 22 agent strategies in...
Machine learning for on-line hardware reconfiguration (2007)
Jonathan Wildstrom, Peter Stone, Emmett Witchel, Mike Dahlin
As computer systems continue to increase in complexity, the need for AI-based solutions is becoming more urgent. For example, high-end servers that can be partitioned into logical subsystems and...
A comparison of two approaches for vision and self-localization on a mobile robot (2007)
Abstract — This paper considers two approaches to the problem of vision and self-localization on a mobile robot. In the first approach, the perceptual processing is primarily bottom-up, with visual...
Machine learning for on-line hardware reconfiguration (2007)
Jonathan Wildstrom, Peter Stone, Emmett Witchel, Mike Dahlin
As computer systems continue to increase in complexity, the need for AI-based solutions is becoming more urgent. For example, high-end servers that can be partitioned into logical subsystems and...
General game learning using knowledge transfer (2007)
Bikramjit Banerjee, Peter Stone
We present a reinforcement learning game player that can interact with a General Game Playing system and transfer knowledge learned in one game to expedite learning in many other games. We use the...
Sharing the road: Autonomous vehicles meet human drivers (2007)
In modern urban settings, automobile traffic and collisions lead to endless frustration as well as significant loss of life, property, and productivity. Recent advances in artificial intelligence...
Jefferson Provost, Benjamin J. Kuipers, Risto Miikkulainen Supervisor, Raymond Mooney, Bruce Porter, Peter Stone, ...
A couple of years ago someone forwarded me a link to an article interpreting Frodo’s quest in The Lord of the Rings as an allegory for writing and filing a dissertation (Lee, 2005). At the time, I...
The chin pinch: A case study in skill learning on a legged robot (2007)
Abstract. When developing skills on a physical robot, it is appealing to turn to modern machine learning methods in order to automate the process. However, when no accurate simulator exists for the...
Color learning on a mobile robot: Towards full autonomy under changing illumination (2007)
A central goal of robotics and AI is to be able to deploy an agent to act autonomously in the real world over an extended period of time. It is commonly asserted that in order to do so, the agent...
General game learning using knowledge transfer (2007)
Bikramjit Banerjee, Peter Stone
We present a reinforcement learning game player that can interact with a General Game Playing system and transfer knowledge learned in one game to expedite learning in many other games. We use the...
Color learning on a mobile robot: Towards full autonomy under changing illumination (2007)
A central goal of robotics and AI is to be able to deploy an agent to act autonomously in the real world over an extended period of time. It is commonly asserted that in order to do so, the agent...
Color learning on a mobile robot: Towards full autonomy under changing illumination (2007)
A central goal of robotics and AI is to be able to deploy an agent to act autonomously in the real world over an extended period of time. It is commonly asserted that in order to do so, the agent...
The UT Austin Villa 3D Simulation Soccer Team 2007 (2007)
Shivaram Kalyanakrishnan, Peter Stone
Abstract. In this paper, we describe the soccer playing agent developed by our team UT Austin Villa to participate in the RoboCup 3D simulation soccer competition held in Atlanta, U.S.A., in July...
Machine learning for on-line hardware reconfiguration (2007)
Jonathan Wildstrom, Peter Stone, Emmett Witchel, Mike Dahlin
As computer systems continue to increase in complexity, the need for AI-based solutions is becoming more urgent. For example, high-end servers that can be partitioned into logical subsystems and...
Model-based exploration in continuous state spaces (2007)
Abstract. Modern reinforcement learning algorithms effectively exploit experience data sampled from an unknown controlled dynamical system to compute a good control policy, but to obtain the...
Batch reinforcement learning in a complex domain (2007)
Shivaram Kalyanakrishnan, Peter Stone
Temporal difference reinforcement learning algorithms are perfectly suited to autonomous agents because they learn directly from an agent’s experience based on sequential actions in the...
Robot Developmental Learning of an Object Ontology Grounded in Sensorimotor Experience (2007)
Joseph Varughese Modayil, Benjamin Kuipers Supervisor, Raymond Mooney, Peter Stone, Brian Stankiewicz, David Kortenkamp, ...
This work would not have been possible without the support of many individuals. My advisor Ben Kuipers has been a source of constant support and stimulation. Without his guidance, my research would...
Sharing the road: Autonomous vehicles meet human drivers (2007)
In modern urban settings, automobile traffic and collisions lead to endless frustration as well as significant loss of life, property, and productivity. Recent advances in artificial intelligence...
Learning for Semantic Parsing with Kernels under Various Forms of Supervision (2007)
Rohit Jaivant Kate, Raymond J. Mooney, Jason Baldridge, Risto Miikkulainen, Dan Roth, Peter Stone, ...
To my parents and sister.
Graph-based domain mapping for transfer learning in general games (2007)
Abstract. A general game player is an agent capable of taking as input a description of a game’s rules in a formal language and proceeding to play without any subsequent human input. To do well, an...
A comparison of two approaches for vision and self-localization on a mobile robot (2007)
Abstract — This paper considers two approaches to the problem of vision and self-localization on a mobile robot. In the first approach, the perceptual processing is primarily bottom-up, with visual...
Autonomous learning of stable quadruped locomotion (2007)
Manish Saggar, Nate Kohl, Peter Stone
Abstract. A fast gait is an essential component of any successful team in the RoboCup 4-legged league. However, quickly moving quadruped robots, including those with learned gaits, often move in such...
Cross-domain transfer for reinforcement learning (2007)
Matthew E. Taylor, Peter Stone
A typical goal for transfer learning algorithms is to utilize knowledge gained in a source task to learn a target task faster. Recently introduced transfer methods in reinforcement learning settings...
Cross-domain transfer for reinforcement learning (2007)
Matthew E. Taylor, Peter Stone
A typical goal for transfer learning algorithms is to utilize knowledge gained in a source task to learn a target task faster. Recently introduced transfer methods in reinforcement learning settings...
A comparison of two approaches for vision and self-localization on a mobile robot (2007)
Abstract — This paper considers two approaches to the problem of vision and self-localization on a mobile robot. In the first approach, the perceptual processing is primarily bottom-up, with visual...
Model-based exploration in continuous state spaces (2007)
Abstract. Modern reinforcement learning algorithms effectively exploit experience data sampled from an unknown controlled dynamical system to compute a good control policy, but to obtain the...
Designing Safe, Profitable Automated Stock Trading Agents Using Evolutionary Algorithms (2006)
Subramanian, Harish, Ramamoorthy, Subramanian, Stone, Peter, Kuipers, Benjamin
Trading rules are widely used by practitioners as an effective means to mechanize aspects of their reasoning about stock price trends. However, due to the simplicity of these rules, each rule is...
Designing safe, profitable automated stock trading agents using evolutionary algorithms (2006)
Harish Subramanian, Subramanian Ramamoorthy, Peter Stone, Benjamin J. Kuipers
Trading rules are widely used by practitioners as an effective means to mechanize aspects of their reasoning about stock price trends. However, due to the simplicity of these rules, each rule is...
Automatic heuristic construction in a complete general game player (2006)
Gregory Kuhlmann, Kurt Dresner, Peter Stone
Computer game players are typically designed to play a single game: today’s best chess-playing programs cannot play checkers, or even tic-tac-toe. General Game Playing is the problem of designing...
Adapting to workload changes through on-the-fly reconfiguration (2006)
Jonathan Wildstrom, Peter Stone, Emmett Witchel, Mike Dahlin
High-end servers that can be partitioned into logical subsystems and repartitioned on the fly are now becoming available. This development raises the possibility of reconfiguring distributed systems...
Adapting to workload changes through on-the-fly reconfiguration (2006)
Peter Stone, Emmett Witchel, Mike Dahlin
Abstract High-end servers that can be partitioned into logical subsystems and repartitioned on the fly are now becomingavailable. This development raises the possibility of reconfiguring distributed...
Value Function Transfer for General Game Playing (2006)
Bikramjit Banerjee, Gregory Kuhlmann, Peter Stone
We present value function transfer techniques for General Game Playing (GGP) by Reinforcement Learning. We focus on 2 player, alternate-move, complete information board games and use the GGP...
Multiagent traffic management: Opportunities for multiagent learning (2006)
Abstract. Traffic congestion is one of the leading causes of lost productivity and decreased standard of living in urban settings. In previous work published at AAMAS, we have proposed a novel...
Autonomous planned color learning on a mobile robot without labeled data (2006)
Color segmentation is a challenging yet integral subtask of mobile robot systems that use visual sensors, especially since they typically have limited computational and memory resources. We present...
Peter Stone, Mohan Sridharan, Daniel Stronger, Gregory Kuhlmann, Nate Kohl, Peggy Fidelman, ...
Mobile robots must cope with uncertainty from many sources along the path from interpreting raw sensor inputs to behavior selection to execution of the resulting primitive actions. This article...
Adapting to workload changes through on-the-fly reconfiguration (2006)
Jonathan Wildstrom, Peter Stone, Emmett Witchel, Mike Dahlin
High-end servers that can be partitioned into logical subsystems and repartitioned on the fly are now becoming available. This development raises the possibility of reconfiguring distributed systems...
Evolving Visibly Intelligent Behavior for Embedded Game Agents (2006)
Bobby D. Bryant, Bobby Don Bryant, Risto Miikkulainen Supervisor, Joydeep Ghosh, Benjamin Kuipers, Raymond Mooney, ...
Copyright by
Designing Safe, Profitable Automated Stock Trading (2006)
Agents Using Evolutionary, Harish Subramanian, Subramanian Ramamoorthy, Peter Stone, Benjamin J. Kuipers
Trading rules are widely used by practitioners as an effective means to mechanize aspects of their reasoning about stock price trends. However, due to the simplicity of these rules, each rule is...
Evolutionary function approximation for reinforcement learning (2006)
Shimon Whiteson, Peter Stone, Sven Koenig, Shie Mannor, Georgios Theocharous
Temporal difference methods are theoretically grounded and empirically effective methods for addressing reinforcement learning problems. In most real-world reinforcement learning tasks, TD methods...
Value Function Transfer for General Game Playing (2006)
Bikramjit Banerjee, Gregory Kuhlmann, Peter Stone
We present value function transfer techniques for General Game Playing (GGP) by Reinforcement Learning. We focus on 2 player, alternate-move, complete information board games and use the GGP...
Value-function-based transfer for reinforcement learning using structure mapping (2006)
Transfer learning concerns applying knowledge learned in one task (the source) to improve learning another related task (the target). In this paper, we use structure mapping, a psychological and...
Keepaway soccer: From machine learning testbed to benchmark (2006)
Peter Stone, Gregory Kuhlmann, Matthew E. Taylor, Yaxin Liu
Abstract. Keepaway soccer has been previously put forth as a testbed for machine learning. Although multiple researchers have used it successfully for machine learning experiments, doing so has...
Adaptive mechanism design: a metalearning approach (2006)
Auction mechanism design has traditionally been a largely analytic process, relying on assumptions such as fully rational bidders. In practice, however, bidders often exhibit unknown and variable...
Evolutionary function approximation for reinforcement learning (2006)
Shimon Whiteson, Peter Stone, Georgios Theocharous
Temporal difference methods are theoretically grounded and empirically effective methods for addressing reinforcement learning problems. In most real-world reinforcement learning tasks, TD methods...
Towards autonomous sensor and actuator model induction on a mobile robot (2006)
1 This article presents a novel methodology for a robot to autonomously induce models of its actions and sensors called asami (Autonomous Sensor and Actuator Model Induction). While previous...
Automatic heuristic construction in a complete general game player (2006)
Gregory Kuhlmann, Kurt Dresner, Peter Stone
Computer game players are typically designed to play a single game: today’s best chess-playing programs cannot play checkers, or even tic-tac-toe. General Game Playing is the problem of designing...
Shimon Whiteson, Matthew E. Taylor, Peter Stone
To excel in challenging tasks, intelligent agents need sophisticated mechanisms for action selection: they need policies that dictate what action to take in each situation. Reinforcement learning...
Peter Stone, Mohan Sridharan, Daniel Stronger, Gregory Kuhlmann, Nate Kohl, Peggy Fidelman, ...
Mobile robots must cope with uncertainty from many sources along the path from interpreting raw sensor inputs to behavior selection to execution of the resulting primitive actions. This article...
Keepaway soccer: From machine learning testbed to benchmark (2006)
Peter Stone, Gregory Kuhlmann, Matthew E. Taylor, Yaxin Liu
Abstract. Keepaway soccer has been previously put forth as a testbed for machine learning. Although multiple researchers have used it successfully for machine learning experiments, doing so has...
Value Function Transfer for General Game Playing (2006)
Bikramjit Banerjee, Gregory Kuhlmann, Peter Stone
We present value function transfer techniques for General Game Playing (GGP) by Reinforcement Learning. We focus on 2 player, alternate-move, complete information board games and use the GGP...
The UT Austin Villa 2006 RoboCup four-legged team,” The (2006)
Peter Stone, Peggy Fidelman, Nate Kohl, Gregory Kuhlmann, Tekin Meriçli, Mohan Sridharan, ...
The UT Austin Villa Four-Legged Team for RoboCup 2006 was a fourth-time entry in the ongoing series of RoboCup legged league competitions. The team development began in mid-January of 2003 without...
Shimon Whiteson, Matthew E. Taylor, Peter Stone
To excel in challenging tasks, intelligent agents need sophisticated mechanisms for action selection: they need policies that dictate what action to take in each situation. Reinforcement learning...
Evolutionary function approximation for reinforcement learning (2006)
Shimon Whiteson, Peter Stone, Sven Koenig, Shie Mannor, Georgios Theocharous
Temporal difference methods are theoretically grounded and empirically effective methods for addressing reinforcement learning problems. In most real-world reinforcement learning tasks, TD methods...
A multi-robot system for continuous area sweeping tasks (2006)
Abstract — As mobile robots become increasingly autonomous over extended periods of time, opportunities arise for their use on repetitive tasks. We define and implement behaviors for a class of...
Know thine enemy: A champion RoboCup coach agent (2006)
Gregory Kuhlmann, William B. Knox, Peter Stone
In a team-based multiagent system, the ability to construct a model of an opponent team’s joint behavior can be useful for determining an agent’s expected distribution over future world states,...
Know thine enemy: A champion RoboCup coach agent (2006)
Gregory Kuhlmann, William B. Knox, Peter Stone
In a team-based multiagent system, the ability to construct a model of an opponent team’s joint behavior can be useful for determining an agent’s expected distribution over future world states,...
Value-function-based transfer for reinforcement learning using structure mapping (2006)
Transfer learning concerns applying knowledge learned in one task (the source) to improve learning another related task (the target). In this paper, we use structure mapping, a psychological and...
A multi-robot system for continuous area sweeping tasks (2006)
Abstract — As mobile robots become increasingly autonomous over extended periods of time, opportunities arise for their use on repetitive tasks. We define and implement behaviors for a class of...
Designing safe, profitable automated stock trading agents using evolutionary algorithms (2006)
Harish Subramanian, Subramanian Ramamoorthy, Peter Stone, Benjamin J. Kuipers
evolutionary algorithms
Automatic heuristic construction in a complete general game player (2006)
Gregory Kuhlmann, Kurt Dresner, Peter Stone
Computer game players are typically designed to play a single game: today’s best chess-playing programs cannot play checkers, or even tic-tac-toe. General Game Playing is the problem of designing...
Transfer learning for policy search methods (2006)
Matthew E. Taylor, Shimon Whiteson, Peter Stone
An ambitious goal of transfer learning is to learn a task faster after training on a different, but related, task. In this paper we extend a previously successful temporal difference (Sutton &...
Designing safe, profitable automated stock trading agents using evolutionary algorithms (2006)
Harish Subramanian, Subramanian Ramamoorthy, Peter Stone, Benjamin J. Kuipers
evolutionary algorithms
TacTex-05: An adaptive agent for TAC SCM (2006)
David Pardoe, Peter Stone, Mark Vanmiddlesworth
Abstract. Supply chains are ubiquitous in the manufacturing of many complex products. Traditionally, supply chains have been created through the interactions of human representatives of the companies...
Research Platform � Sony Aibo (Artificial Intelligence roBOt) (2006)
� To make the robot walk � To make the robot learn to walk � To make the robot learn to walk fast
Bowker, Michael, Stone, Peter, Morrall, Peter, Smith, Rupert, Bennett, Roger, Perkins, Neil, ...
Model catalysts of Pd nanoparticles and films on TiO2(110) were fabricated by metal vapour deposition (MVD). Molecular beam measurements show that the particles are active for CO adsorption, with a...
Bowker, Michael, Stone, Peter, Morrall, Peter, Smith, Rupert, Bennett, Roger, Perkins, Neil, ...
Model catalysts of Pd nanoparticles and films on TiO2(110) were fabricated by metal vapour deposition (MVD). Molecular beam measurements show that the particles are active for CO adsorption, with a...
Thesis (S.B.)--Massachusetts Institute of Technology, Dept. of Materials Science and Engineering, 2005.
Thesis (S.B.)--Massachusetts Institute of Technology, Dept. of Materials Science and Engineering, 2005.
The harmonic force balance method was used to model and simulate electric force microscopy (EFM) and electrostatically generated phase difference in tapping mode AFM (EPTA) measurements. Simulations...
Simultaneous calibration of action and sensor models on a mobile robot (2005)
Abstract--- This paper presents a technique for the Simultaneous
Traffic congestion is one of the leading causes of lost productivity and decreased standard of living in urban settings. Recent advances in artificial intelligence suggest vehicle navigation by...
Towards self-configuring hardware for distributed computer systems (2005)
Jonathan Wildstrom, Peter Stone, Emmett Witchel, Raymond J. Mooney, Mike Dahlin
High-end servers that can be partitioned into logical subsystems and repartitioned on the fly are now becoming available. This development raises the possibility of reconfiguring distributed systems...
The ut austin villa 2003 champion simulator coach: A machine learning approach (2005)
Gregory Kuhlmann, Peter Stone, Justin Lallinger
Abstract. The UT Austin Villa 2003 simulated online soccer coach was a first time entry in the RoboCup Coach Competition. In developing the coach, the main research focus was placed on treating...
Evolving keepaway soccer players through task decomposition (2005)
Shimon Whiteson, Nate Kohl, Risto Miikkulainen, Peter Stone
Abstract. In some complex control tasks, learning a direct mapping from an agent's sensors to its actuators is very dicult. For such tasks, decomposing the problem into more manageable...
Simultaneous calibration of action and sensor models on a mobile robot (2005)
Abstract — This paper presents a technique for the Simultaneous
Practical vision-based Monte Carlo localization on a legged robot (2005)
Mohan Sridharan, Gregory Kuhlmann, Peter Stone
Abstract — Mobile robot localization, the ability of a robot to determine its position and orientation in a global frame of reference, continues to be a major research focus in robotics. In most...
Layered Learning on a Physical Robot (2005)
Abstract — Layered learning is a general hierarchical machine learning paradigm that leverages a given task decomposition to learn complex tasks efficiently. Though it has been validated previously...
Evolving keepaway soccer players through task decomposition (2005)
Shimon Whiteson, Nate Kohl, Risto Miikkulainen, Peter Stone
1 Introduction One of the goals of machine learning algorithms is to facilitate the discovery of novel solutions to problems, particularly those that might be unforeseen by human problem-solvers. As...
Towards self-configuring hardware for distributed computer systems (2005)
Jonathan Wildstrom, Peter Stone, Emmett Witchel, Raymond J. Mooney, Computer Sciences
1.
Towards self-configuring hardware for distributed computer systems (2005)
Jonathan Wildstrom, Peter Stone, Emmett Witchel, Raymond J. Mooney, Mike Dahlin
High-end servers that can be partitioned into logical subsystems and repartitioned on the fly are now becoming available. This development raises the possibility of reconfiguring distributed systems...
The UT Austin Villa 2005 RoboCup four-legged team (2005)
Peter Stone, Kurt Dresner, Peggy Fidelman, Nate Kohl, Gregory Kuhlmann, Mohan Sridharan, ...
The UT Austin Villa Four-Legged Team for RoboCup 2005 was a third-time entry in the ongoing series of RoboCup legged league competitions. The team development began in mid-January of 2003 without any...
Reinforcement learning for RoboCup-soccer keepaway (2005)
Peter Stone, Richard S. Sutton, Gregory Kuhlmann
1 RoboCup simulated soccer presents many challenges to reinforcement learning methods, in-cluding a large state space, hidden and uncertain state, multiple independent agents learning simultaneously,...
Simultaneous calibration of action and sensor models on a mobile robot (2005)
Abstract — This paper presents a technique for the Simultaneous
A model-based approach to robot joint control (2005)
Abstract. Despite efforts to design precise motor controllers, robot joints do not always move exactly as desired. This paper introduces a general model-based method for improving the accuracy of...
Prem Noel Melville, Raymond J. Mooney, Benjamin Kuipers, Peter Stone, Joydeep Ghosh, Jude Shavlik, ...
To my loving parents.
Towards self-configuring hardware for distributed computer systems (2005)
Jonathan Wildstrom, Peter Stone, Emmett Witchel, Raymond J. Mooney, Mike Dahlin
High-end servers that can be partitioned into logical subsystems and repartitioned on the fly are now becoming available. This development raises the possibility of reconfiguring distributed systems...
Developing Adaptive Auction Mechanisms (2005)
Mechanism design has traditionally been a largely analytic process, relying on assumptions such as fully rational bidders. In practice, however, these assumptions may not hold, making bidder behavior...
Function approximation via tile coding: Automating parameter choice (2005)
Abstract. Reinforcement learning (RL) is a powerful abstraction of sequential decision making that has an established theoretical foundation and has proven effective in a variety of small, simulated...
A model-based approach to robot joint control (2005)
Abstract. Despite efforts to design precise motor controllers, robot joints do not always move exactly as desired. This paper introduces a general model-based method for improving the accuracy of...
Towards self-configuring hardware for distributed computer systems (2005)
Peter Stone, Emmett Witchel, Raymond J. Mooney, Computer Sciences
1.
Prem Noel Melville, Raymond J. Mooney, Benjamin Kuipers, Peter Stone, Joydeep Ghosh, Jude Shavlik, ...
To my loving parents.
Three Degrees of Freedom (2005)
Nate Kohl, Peter Stone, Pawel Pytlak, Power Utilization
◮ One of main goals in the field of robotics is to be able to deploy a fully autonomous robot in an unstructured dynamic environment for an extended period of time. ◮ Work is done to improve:
Evolving keepaway soccer players through task decomposition (2005)
Shimon Whiteson, Nate Kohl, Risto Miikkulainen, Peter Stone
Abstract. In some complex control tasks, learning a direct mapping from an agent’s sensors to its actuators is very difficult. For such tasks, decomposing the problem into more manageable...
On Continuous-Action Q-Learning via Tile Coding Function Approximation (2004)
Reinforcement learning (RL) is a powerful machine-learning methodology that has an established theoretical foundation and has proven effective in a variety of small, simulated domains. There has been...
Learning ball acquisition on a physical robot (2004)
Abstract--- For a robot to learn to improve its performance based entirely on real-world environmental feedback, the robot's behavior specification and learning algorithm must be constructed so...
Three automated stock-trading agents: A comparative study (2004)
Abstract. This paper documents the development of three autonomous stocktrading agents within the framework of the Penn Exchange Simulator (PXS), a novel stock-trading simulator that takes advantage...
Towards illumination invariance in the legged league (2004)
Abstract. To date, RoboCup games have all been played under constant, bright lighting conditions. However, in order to meet the overall goal of RoboCup, robots will need to be able to seamlessly...
Automated Stock Trading (2004)
Harish K Subramanian, Harish K Subramanian, Peter Stone, Benjamin Kuipers, Joydeep Ghosh, Harish K Subramanian, ...
Acknowledgements I would like to express my deep gratitude to Dr. Peter Stone for his guidance, advice, and encouragement. It has been great fun and a privilege to conduct research under his...
Gregory Kuhlmann, Peter Stone, Raymond Mooney
We describe our current efforts towards creating a reinforcement learner that learns both from reinforcements provided by its environment and from human-generated advice. Our research involves two...
Machine learning for fast quadrupedal locomotion (2004)
For a robot, the ability to get from one place to another is one of the most basic skills. However, locomotion on legged robots is a challenging multidimensional control problem. This paper presents...
Progress in Learning 3 vs. 2 Keepaway (2004)
Abstract. Reinforcement learning has been successfully applied to several subtasks in the RoboCup simulated soccer domain. Keepaway is one such task. One notable success in the keepaway domain has...
Progress in Learning 3 vs. 2 Keepaway (2004)
Abstract. Reinforcement learning has been successfully applied to several subtasks in the RoboCup simulated soccer domain. Keepaway is one such task. One notable success in the keepaway domain has...
Learning ball acquisition on a physical robot (2004)
Abstract — For a robot to learn to improve its performance based entirely on real-world environmental feedback, the robot’s behavior specification and learning algorithm must be constructed so as...
Machine learning for fast quadrupedal locomotion (2004)
For a robot, the ability to get from one place to another is one of the most basic skills. However, locomotion on legged robots is a challenging multidimensional control problem. This paper presents...
Two stock-trading agents: Market making and technical analysis (2004)
Yi Feng, Ronggang Yu, Peter Stone
Evolving information technologies have brought computational power and real-time facilities into the stock market. Automated stock trading draws much interest from both the elds of computer science...
Progress in Learning 3 vs. 2 Keepaway (2004)
Abstract. Reinforcement learning has been successfully applied to several subtasks in the RoboCup simulated soccer domain. Keepaway is one such task. One notable success in the keepaway domain has...
The UT Austin Villa 2003 four-legged team (2004)
Peter Stone, Kurt Dresner, Selim T. Erdogan, Peggy Fidelman, Nicholas K. Jong, Nate Kohl, ...
a new entry in the ongoing series of RoboCup legged league competitions. The team development began in mid-January of 2003, at which time none of the team members had any familiarity with the Aibos....
Progress in Learning 3 vs. 2 Keepaway (2004)
Peter Stone, Richard S. Sutton, Satinder Singh
Abstract. As a sequential decision problem, robotic soccer can bene t from research in reinforcement learning. We introduce the 3 vs. 2 keepaway domain, a subproblem of robotic soccer implemented in...
Progress in Learning 3 vs. 2 Keepaway (2004)
Peter Stone, Richard S. Sutton, Satinder Singh
Abstract. As a sequential decision problem, robotic soccer can bene t from research in reinforcement learning. We introduce the 3 vs. 2 keepaway domain, a subproblem of robotic soccer implemented in...
The UT Austin Villa 2003 Champion Simulator Coach: A Machine Learning Approach (2004)
Gregory Kuhlmann, Peter Stone, Justin Lallinger
The UT Austin Villa 2003 simulated online soccer coach was a rst time entry in the RoboCup Coach Competition. In developing the coach, the main research focus was placed on treating advice-giving as...
Bidding for Customer Orders in TAC SCM (2004)
David Pardoe And, David Pardoe, Peter Stone
Supply chains are a current, challenging problem for agentbased electronic commerce. Motivated by the Trading Agent Competition Supply Chain Management (TAC SCM) scenario, we consider an individual...
Towards Autonomic Computing: Adaptive Job Routing (2004)
And Scheduling Shimon, Shimon Whiteson, Peter Stone
Computer systems are rapidly becoming so complex that maintaining them with human support stas will be prohibitively expensive and inecient. In response, visionaries have begun proposing that...
Gregory Kuhlmann, Peter Stone, Raymond Mooney, Jude Shavlik
We describe our current efforts towards creating a reinforcement learner that learns both from reinforcements provided by its environment and from human-generated advice. Our research involves two...
Guiding a Reinforcement Learner with Natural Language Advice: (2004)
Initial Results In, Gregory Kuhlmann, Peter Stone, Raymond Mooney, Jude Shavlik
We describe our current efforts towards creating a reinforcement learner that learns both from reinforcements provided by its environment and from human-generated advice. Our research involves two...
A Model-Based Approach to Robot Joint Control (2004)
Daniel Stronger And, Daniel Stronger, Peter Stone
Despite eorts to design precise motor controllers, robot joints do not always move exactly as desired. This paper introduces a general model-based method for improving the accuracy of joint control.
Policy Gradient Reinforcement Learning for Fast Quadrupedal Locomotion (2004)
This paper presents a machine learning approach to optimizing a quadrupedal trot gait for forward speed. Given a parameterized walk designed for a specific robot, we propose using a form of policy...
Two Stock-Trading Agents: Market Making and Technical Analysis (2004)
Yi Feng, Ronggang Yu, Peter Stone
Evolving information technologies have brought computational power and real-time facilities into the stock market. Automated stock trading draws much interest from both the elds of computer science...
Speeding up Reinforcement Learning with (2004)
Behavior Transfer Matthew, Matthew E. Taylor, Peter Stone
Reinforcement learning (RL) methods (Sutton & Barto 1998) have become popular machine learning techniques in recent years. RL has had some experimental successes and has been shown to exhibit...
Progress in Learning 3 vs. 2 Keepaway (2004)
Abstract. Reinforcement learning has been successfully applied to several subtasks in the RoboCup simulated soccer domain. Keepaway is one such task. One notable success in the keepaway domain has...
Gregory Kuhlmann, Peter Stone, Raymond Mooney
We describe our current efforts towards creating a reinforcement learner that learns both from reinforcements provided by its environment and from human-generated advice. Our research involves two...
Towards illumination invariance in the legged league (2004)
Abstract. To date, RoboCup games have all been played under constant, bright lighting conditions. However, in order to meet the overall goal of RoboCup, robots will need to be able to seamlessly...
Policy gradient reinforcement learning for fast quadrupedal locomotion (2004)
Abstract — This paper presents a machine learning approach to optimizing a quadrupedal trot gait for forward speed. Given a parameterized walk designed for a specific robot, we propose using a form...
Adaptive job routing and scheduling (2004)
Computer systems are rapidly becoming so complex that maintaining them with human support staffs will be prohibitively expensive and inefficient. In response, visionaries have begun proposing that...
Adaptive job routing and scheduling (2004)
Computer systems are rapidly becoming so complex that maintaining them with human support staffs will be prohibitively expensive and inefficient. In response, visionaries have begun proposing that...
Machine learning for fast quadrupedal locomotion (2004)
For a robot, the ability to get from one place to another is one of the most basic skills. However, locomotion on legged robots is a challenging multidimensional control problem. This paper presents...
Bidding for customer orders in TAC SCM (2004)
Abstract. Supply chains are a current, challenging problem for agentbased electronic commerce. Motivated by the Trading Agent Competition Supply Chain Management (TAC SCM) scenario, we consider an...
Three automated stock-trading agents: A comparative study (2004)
Abstract. This paper documents the development of three autonomous stocktrading agents within the framework of the Penn Exchange Simulator (PXS), a novel stock-trading simulator that takes advantage...
Gregory Kuhlmann, Peter Stone, Raymond Mooney
We describe our current efforts towards creating a reinforcement learner that learns both from reinforcements provided by its environment and from human-generated advice. Our research involves two...
Machine learning for fast quadrupedal locomotion (2004)
For a robot, the ability to get from one place to another is one of the most basic skills. However, locomotion on legged robots is a challenging multidimensional control problem. This paper presents...
Policy gradient reinforcement learning for fast quadrupedal locomotion (2004)
Abstract — This paper presents a machine learning approach to optimizing a quadrupedal trot gait for forward speed. Given a parameterized walk designed for a specific robot, we propose using a form...
Multiagent Traffic Management: A Reservation-Based Intersection Control Mechanism (2004)
productivity and decreased standard of living in urban settings. Recent advances in artificial intelligence suggest vehicle navigation by autonomous agents will be possible in the near future. In...
Machine Learning for Fast Quadrupedal Locomotion (2004)
Nate Kohl And, Nate Kohl, Peter Stone
For a robot, the ability to get from one place to another is one of the most basic skills. However, locomotion on legged robots is a challenging multidimensional control problem.
TacTex-03: A Supply Chain Management Agent (2004)
David Pardoe And, David Pardoe, Peter Stone
This paper introduces TacTex-03, an agent designed to participate in the Trading Agent Competition Supply Chain Management Scenario (TAC SCM). As speci ed by this scenario, TacTex-03 acts as a...
Towards employing PSRs in a continuous domain (2004)
Predictive State Representations (PSRs) recently emerged as an alternative framework for reasoning about stochastic environments. However, unlike Markov decision processes, they have not yet been...
The 2001 Trading Agent Competition (2003)
Michael P. Wellman, Amy Greenwald, Peter R. Wurman, Peter Stone
The 2001 Trading Agent Competition was the second in a series of events aiming to shed light on research issues in automating trading strategies. Based on a challenging market scenario in the domain...
The RoboCup soccer server and CMUnited clients: Implemented infrastructure for MAS research (2003)
2 1.
A polynomial-time Nash equilibrium algorithm for repeated games (2003)
Michael L. Littman, Peter Stone
With the increasing reliance on game theory as a foundation for auctions and electronic commerce, ecient algorithms for computing equilibria in multiplayer general-sum games are of great theoretical...
P.: RoboCup in Higher Education: A preliminary report (2003)
Elizabeth Sklar, Simon Parsons, Peter Stone
Abstract. Since team-based projects have been proven to be an effective pedagogical tool, we have been using RoboCup challenges as the basis for class projects in undergraduate courses. This paper...
Learning predictive state representations (2003)
Satinder Singh, Michael L. Littman, Nicholas K. Jong, David Pardoe, Peter Stone
We introduce the rst algorithm for learning predictive state representations (PSRs), which are a way of representing the state of a controlled dynamical system. The state representation in a PSR is a...
P.: Performance analysis of a counter-intuitive automated stock-trading agent (2003)
Autonomous trading in the stock market is an area of great interest in both academic and commercial circles. A lot of trading strategies have been proposed and practiced from the perspectives of...
A polynomial-time Nash equilibrium algorithm for repeated games (2003)
Michael L. Littman, Peter Stone
With the increasing reliance on game theory as a foundation for auctions and electronic commerce, e#cient algorithms for computing equilibria in multiplayer general-sum games are of great theoretical...
Joon Woo Kim, K. Suzanne Barber, Ari Arapostathis, Margarida Jacome, Peter Stone, Elmira Popova, ...
my wife, Kyung, and my daughters, Christine and Erin for their love, support, and encouragement Acknowledgements This dissertation is the product not only of my own thinking and writing but also...
The RoboCup soccer server and CMUnited clients: Implemented infrastructure for MAS research (2003)
nodaet 1. go. j p
Learning predictive state representations (2003)
Satinder Singh, Michael L. Littman, Nicholas K. Jong, David Pardoe, Peter Stone
We introduce the rst algorithm for learning predictive state representations (PSRs), which are a way of representing the state of a controlled dynamical system. The state representation in a PSR is a...
The Champion UT Austin Villa 2003 Simulator Online Coach Team (2003)
Gregory Kuhlmann, Peter Stone, Justin Lallinger
The UT Austin Villa 2003 simulated online soccer coach was a rst time entry in the RoboCup Coach Competition. In developing the coach, the main research focus was placed on treating advice-giving as...
Learning Predictive State Representations (2003)
Satinder Singh, Michael L. Littman, Peter Stone, Richard Sutton
We introduce the first algorithm for learning predictive state representations.
P.: RoboCup in Higher Education: A preliminary report (2003)
Elizabeth Sklar, Simon Parsons, Peter Stone
Abstract. Since team-based projects have been proven to be an effective pedagogical tool, we have been using RoboCup challenges as the basis for class projects in undergraduate courses. This paper...
Proposed establishment of a Bio-Diesel Production facility (Feasibility) (2002)
Garner, Gary O., Hollingsworth, Peter, Stone, Peter
The outcome of this Report suggests there is strong viability for the establishment of a 60,000 tonne p.a. (raw material input) biodiesel plant.A plant of this size would provide an annual output (at...
Proposed establishment of a Bio-Diesel Production facility (Feasibility) (2002)
Garner, Gary O., Hollingsworth, Peter, Stone, Peter
The outcome of this Report suggests there is strong viability for the establishment of a 60,000 tonne p.a. (raw material input) biodiesel plant.A plant of this size would provide an annual output (at...
Proposed establishment of a Bio-Diesel Production facility (Feasibility) (2002)
Garner, Gary O., Hollingsworth, Peter, Stone, Peter
The outcome of this Report suggests there is strong viability for the establishment of a 60,000 tonne p.a. (raw material input) biodiesel plant.A plant of this size would provide an annual output (at...
Proposed establishment of a Bio-Diesel Production facility (Feasibility) (2002)
Garner, Gary O., Hollingsworth, Peter, Stone, Peter
The outcome of this Report suggests there is strong viability for the establishment of a 60,000 tonne p.a. (raw material input) biodiesel plant.A plant of this size would provide an annual output (at...
Proposed establishment of a Bio-Diesel Production facility (Feasibility) (2002)
Garner, Gary O., Hollingsworth, Peter, Stone, Peter
The outcome of this Report suggests there is strong viability for the establishment of a 60,000 tonne p.a. (raw material input) biodiesel plant.A plant of this size would provide an annual output (at...
Proposed establishment of a Bio-Diesel Production facility (Feasibility) (2002)
Garner, Gary O., Hollingsworth, Peter, Stone, Peter
The outcome of this Report suggests there is strong viability for the establishment of a 60,000 tonne p.a. (raw material input) biodiesel plant.A plant of this size would provide an annual output (at...
Proposed establishment of a Bio-Diesel Production facility (Feasibility) (2002)
Garner, Gary O., Hollingsworth, Peter, Stone, Peter
The outcome of this Report suggests there is strong viability for the establishment of a 60,000 tonne p.a. (raw material input) biodiesel plant.A plant of this size would provide an annual output (at...
Self-enforcing strategic demand reduction (2002)
Peter Stone, János A. Csirik, Michael L. Littman
Abstract. Auctions are an area of great academic and commercial interest, from tiny auctions for toys on eBay to multi-billion-dollar auctions held by governments for resources or contracts. Although...
ATTac-2001: A learning, autonomous bidding agent (2002)
Peter Stone, Robert E. Schapire, János A. Csirik, Michael L. Littman, David Mcallester
Abstract. Auctions are becoming an increasingly popular method for transacting business, especially over the Internet. This paper presents a general approach to building autonomous bidding agents to...
Randomized strategic demand reduction: Getting more by asking for less (2002)
Peter Stone, Janos A. Csirik, Michael L. Littman
Auctions are an area of great academic and commercial interest, from tiny auctions for toys on eBay to multi-billiondollar auctions held by governments for resources or contracts. Although there has...
Keepaway soccer: a machine learning testbed (2002)
Peter Stone, Richard S. Sutton
Abstract. RoboCup simulated soccer presents many challenges to machine learning (ML) methods, including a large state space, hidden and uncertain state, multiple agents, and long and variable delays...
Multiagent competitions and research: Lessons from RoboCup and TAC (2002)
Abstract. This paper compares and contrasts two recent series of competitions in which multiple agents compete directly against one another: the robot soccer world cup (RoboCup) and the trading agent...
The 2001 trading agent competition (2002)
Michael P. Wellman, Amy Greenwald, Peter Stone, Peter R. Wurman
The 2001 Trading Agent Competition was the second in a series of events aiming to shed light on research issues in automating trading strategies. Based on a challenging market scenario in the domain...
Modeling auction price uncertainty using boosting-based conditional density estimation (2002)
Robert E. Schapire, Peter Stone, David Mcallester, Michael L. Littman
In complicated, interacting auctions, a fundamental problem is the prediction of prices of goods in the auctions, and more broadly, the modeling of uncertainty regarding these prices. In this paper,...
Self-enforcing Strategic Demand Reduction (2002)
Paul Reitsma Peter, Peter Stone, Michael L. Littman
Auctions are an area of great academic and commercial interest, from tiny auctions for toys on eBay to multi-billion-dollar auctions held by governments for resources or contracts. Although there has...
Robert E. Schapire, Peter Stone, David Mcallester, Michael L. Littman
In complicated, interacting auctions, a fundamental problem is the prediction of prices of goods in the auctions, and more broadly, the modeling of uncertainty regarding these prices. In this paper,...
The 2001 Trading Agent Competition (2002)
Michael P. Wellman, Peter Stone, Amy Greenwald, Peter R. Wurman
The 2001 Trading Agent Competition was the second in a series of events aiming to shed light on research issues in automating trading strategies. Based on a challenging market scenario in the domain...
Modeling auction price uncertainty using boosting-based conditional density estimation (2002)
Robert E. Schapire, Peter Stone, David Mcallester, Michael L. Littman, János A. Csirik
In complicated, interacting auctions, a fundamental problem is the prediction of prices of goods in the auctions, and more broadly, the modeling of uncertainty regarding these prices. In this paper,...
Self-enforcing strategic demand reduction (2002)
Peter Stone, János A. Csirik, Michael L. Littman
Abstract. Auctions are an area of great academic and commercial interest, from tiny auctions for toys on eBay to multi-billion-dollar auctions held by governments for resources or contracts. Although...
Self-enforcing Strategic Demand Reduction (2002)
Paul Reitsma Peter, Peter Stone, János A. Csirik, Michael L. Littman
Auctions are an area of great academic and commercial interest, from tiny auctions for toys on eBay to multi-billion-dollar auctions held by governments for resources or contracts. Although there has...
Modeling auction price uncertainty using boosting-based conditional density estimation (2002)
Robert E. Schapire, Peter Stone, David Mcallester, Michael L. Littman, János A. Csirik
In complicated, interacting auctions, a fundamental problem is the prediction of prices of goods in the auctions, and more broadly, the modeling of uncertainty regarding these prices. In this paper,...
Proposed establishment of a Bio-Diesel Production facility (Feasibility) (2002)
Garner, Gary O., Hollingsworth, Peter, Stone, Peter
The outcome of this Report suggests there is strong viability for the establishment of a 60,000 tonne p.a. (raw material input) biodiesel plant.A plant of this size would provide an annual output (at...
Proposed establishment of a Bio-Diesel Production facility (Feasibility) (2002)
Garner, Gary O., Hollingsworth, Peter, Stone, Peter
The outcome of this Report suggests there is strong viability for the establishment of a 60,000 tonne p.a. (raw material input) biodiesel plant.A plant of this size would provide an annual output (at...
A social reinforcement learning agent (2001)
Charles Lee Isbell, R. Shelton, Michael Kearns, Satinder Singh, Peter Stone
software agent that resides in the well-known online chat community LambdaMOO. Our initial work on Cobot (Isbell et al., 2000) provided him with the ability to collect social statistics and report...
FAucS: An FCC spectrum auction simulator for autonomous bidding agents (2001)
János A. Csirik, Michael L. Littman, Satinder Singh, Peter Stone
Abstract. We introduce FAucS, a software testbed for studying automated agent bidding strategies in simulated auctions, specifically the United States FCC wireless frequency spectrum auctions. In...
Autonomous Bidding Agents (2001)
This article summarizes the bidding algorithms developed for the on-line Trading Agent Competition held in July, 2000 in Boston. At its heart, the article describes 12 of the 22 agent strategies in...
A social reinforcement learning agent (2001)
Charles Lee Isbell, R. Shelton, Michael Kearns, Satinder Singh, Peter Stone
We report on our reinforcement learning work on Cobot, a software agent that resides in the well-known online chat community LambdaMOO. Our initial work on Cobot (Isbell et al., 2000) provided him...
ATTac-2000: An adaptive autonomous bidding agent (2001)
Peter Stone, Michael L. Littman
The First Trading Agent Competition (TAC) was held from June 22nd to July 8th, 2000. TAC was designed to create a benchmark problem in the complex domain of emarketplaces and to motivate researchers...
Cobot: A social reinforcement learning agent (2001)
Charles Lee Isbell, R. Shelton, Michael Kearns, Satinder Singh, Peter Stone
We report on the use of reinforcement learning with Cobot, a software agent residing in the well-known online community LambdaMOO. Our initial work on Cobot (Isbell et al.2000) provided him with the...
Implicit negotiation in repeated games (2001)
Michael L. Littman, Peter Stone
Abstract. In business-related interactions such as the on-going highstakes FCC spectrum auctions, explicit communication among participants is regarded as collusion, and is therefore illegal. In this...
Leading best-response strategies in repeated games (2001)
Michael L. Littman, Peter Stone
In repeated general-sum games, an agent using a \best response " strategy maximizes its own payo assuming its behavior has no eect on its opponent. This notion of best response requires some...
ATTac-2000: An adaptive autonomous bidding agent (2001)
Peter Stone, Michael L. Littman
The First Trading Agent Competition (TAC) was held from June 22nd to July 8th, 2000. TAC was designed to create a benchmark problem in the complex domain of emarketplaces and to motivate researchers...
Cobot: A social reinforcement learning agent (2001)
Charles Lee Isbell, R. Shelton, Michael Kearns, Satinder Singh, Peter Stone
We report on the use of reinforcement learning with Cobot, a software agent residing in the wellknown online community LambdaMOO. Our initial work on Cobot (Isbell et al.2000) provided him with the...
FAucS: An FCC spectrum auction simulator for autonomous bidding agents (2001)
Michael L. Littman, Satinder Singh, Peter Stone
We introduce FAucS, a software testbed for studying automated agent bidding strategies in simulated auctions, specifically the United States FCC wireless frequency spectrum auctions. In addition to...
ATTac-2000: An adaptive autonomous bidding agent (2001)
Peter Stone, Michael L. Littman
The First Trading Agent Competition (TAC) was held from June 22nd to July 8th, 2000. TAC was designed to create a benchmark problem in the complex domain of emarketplaces and to motivate researchers...
An architecture for action selection in robotic soccer (2001)
CMUnited-99 was the 1999 RoboCup robotic soccer simulator league champion. In the RoboCup-2000 competition, CMUnited-99 was entered again and despite being publicly available for the entire year, it...
Scaling reinforcement learning toward Robocup soccer (2001)
Peter Stone, Richard S. Sutton, Gregory Kuhlmann
Editor: ed RoboCup simulated soccer presents many challenges to reinforcement learning methods, including a large state space, hidden and uncertain state, multiple agents, and long and variable...
Layered Disclosure: Revealing Agents' Internals (2001)
Patrick Riley, Peter Stone, Manuela Veloso
A perennial challenge in creating and using complex autonomous agents is following their choices of actions as the world changes dynamically and understanding why they act as they do. This paper...
An architecture for action selection in robotic soccer (2001)
content areas: multi-agent teams, coordinating multiple agents,action selection and planning, real-time performance
Cobot: A social reinforcement learning agent (2001)
Charles Lee Isbell, R. Shelton, Michael Kearns, Satinder Singh, Peter Stone
We report on the use of reinforcement learning with Cobot, a software agent residing in the well-known online community LambdaMOO. Our initial work on Cobot (Isbell et al.2000) provided him with the...
ATTac-2000: An adaptive autonomous bidding agent (2001)
Peter Stone, Michael L. Littman, Satinder Singh, Michael Kearns
The First Trading Agent Competition (TAC) was held from June 22nd to July 8th, 2000. TAC was designed to create a benchmark problem in the complex domain of emarketplaces and to motivate researchers...
An architecture for action selection in robotic soccer (2001)
ABSTRACT CMUnited-99 was the 1999 RoboCup robotic soccer simulator league champion. In the RoboCup-2000 competition, CMUnited-99 was entered again and despite being publicly available for the entire...
Scaling reinforcement learning toward Robocup soccer (2001)
Peter Stone, Richard S. Sutton
RoboCup simulated soccer presents many challenges to reinforcement learning methods, including a large state space, hidden and uncertain state, multiple agents, and long and variable delays in the e...
Keeping the ball from CMUnited-99 (2001)
Abstract. This paper presents preliminary results achieved during our current development of a team for simulated robotic soccer in the RoboCup soccer server [2]. We have constructed a team that...
Scaling reinforcement learning toward Robocup soccer (2001)
Peter Stone, Richard S. Sutton
RoboCup simulated soccer presents many challenges to reinforcement learning methods, including a large state space, hidden and uncertain state, multiple agents, and long and variable delays in the...
Autonomous bidding agents in the Trading Agent Competition (2001)
Designing agents that can bid in online simultaneous auctions is a complex task.The authors describe task-specific details and strategies of agents in a trading agent competition. Anatural offshoot...
A social reinforcement learning agent (2001)
Charles Lee Isbell, R. Shelton, Michael Kearns, Satinder Singh, Peter Stone
software agent that resides in the well-known online chat community LambdaMOO. Our initial work on Cobot (Isbell et al., 2000) provided him with the ability to collect social statistics and report...
A social reinforcement learning agent (2001)
Charles Lee Isbell, R. Shelton, Michael Kearns, Satinder Singh, Peter Stone
We report on our reinforcement learning work on Cobot, a software agent that resides in the well-known online chat community LambdaMOO. Our initial work on Cobot (Isbell et al., 2000) provided him...
Cobot: A social reinforcement learning agent (2001)
Charles Lee Isbell, R. Shelton, Michael Kearns, Satinder Singh, Peter Stone
We report on the use of reinforcement learning with Cobot, a software agent residing in the wellknown online community LambdaMOO. Our initial work on Cobot (Isbell et al.2000) provided him with the...
Layered disclosure: Revealing agents’ internals (2000)
Patrick Riley, Peter Stone, Manuela Veloso
Abstract. A perennial challenge in creating and using complex autonomous agents is following their choices of actions as the world changes dynamically and understanding why they act as they do. This...
Defining and using ideal teammate and opponent agent models (2000)
A common challenge for agents in multiagent systems is trying to predict what other agents are going to do in the future. Such knowledge can help an agent determine which of its current action...
Multiagent systems: A survey from a machine learning perspective (2000)
www.research.att.com/˜pstone www.cs.cmu.edu/˜mmv
Multiagent systems: A survey from a machine learning perspective (2000)
Distributed Artificial Intelligence (DAI) has existed as a subfield of AI for less than two decades. DAI is concerned with systems that consist of multiple independent entities that interact in a...
Hierarchies are powerful tools for decomposing complex control tasks into manageable subtasks. Several hierarchical approaches have been proposed for creating agents that can execute these tasks....
CMUnited-98: RoboCup-98 small-robot world champion team (2000)
Manuela Veloso, Michael Bowling, Sorin Achim, Kwun Han, Peter Stone
The CMUnited small-robot team became the 1998 RoboCup small-robot league champion, repeating its 1997 victory. CMUnited-98 built upon the success of CMUnited-97, and involved a number of...
This paper presents layered learning, a hierarchical machine learning paradigm. Layered learning applies to tasks for which learning a direct mapping from inputs to outputs is intractable with...
Layered Disclosure: Revealing Agents' Internals (2000)
Patrick Riley Peter, Peter Stone, Manuela Veloso
A perennial challenge in creating and using complex autonomous agents is following their choices of actions as the world changes dynamically and understanding why they act as they do. This paper...
Defining and Using Ideal Teammate and Opponent Agent Models (2000)
Peter Stone, Patrick Riley, Manuela Veloso
A common challenge for agents in multiagent systems is trying to predict what other agents are going to do in the future. Such knowledge can help an agent determine which of its current action...
This paper presents "layered learning," a hierarchical machine learning paradigm. Layered learning applies to tasks for which learning a direct mapping from inputs to outputs is in...
Multiagent systems: A survey from a machine learning perspective (2000)
www.research.att.com/˜pstone www.cs.cmu.edu/˜mmv
Defining and using ideal teammate and opponent agent models (2000)
A common challenge for agents in multiagent systems is trying to predict what other agents are going to do in the future. Such knowledge can help an agent determine which of its current action...
TPOT-RL applied to network routing (2000)
Team-partitioned, opaque-transition reinforcement learning (TPOT-RL) is a distributed reinforcement learning technique that allows a team of independent agents to learn a collaborative task. TPOT-RL...
Layered disclosure: Revealing agents’ internals (2000)
Patrick Riley, Peter Stone, Ý Manuela Veloso
Abstract. A perennial challenge in creating and using complex autonomous agents is following their choices of actions as the world changes dynamically and understanding why they act as they do. This...
Defining and Using Ideal Teammate and Opponent Agent Models: A Case Study in Robotic Soccer (2000)
Peter Stone, Patrick Riley, Manuela Veloso
this paper, we introduce "ideal-model-based behavior outcome prediction" (IMBBOP). This technique predicts an agent's future actions in relation to the optimal behavior in its given...
TPOT-RL applied to network routing (2000)
Team-partitioned, opaque-transition reinforcement learning (TPOT-RL) is a distributed reinforcement learning technique that allows a team of independent agents to learn a collaborative task. TPOT-RL...
The CMUnited-98 champion small robot team (1999)
Manuela Veloso, Michael Bowling, Sorin Achim, Kwun Han, Peter Stone
Abstract. In this chapter, we present the main research contributions of our champion CMUnited-98 small robot team. The team is a multiagent robotic system with global perception, and distributed...
Anticipation as a key for collaboration in a team of agents: A case study in robotic soccer (1999)
Manuela Veloso, Peter Stone, Michael Bowling
We investigate teams of complete autonomous agents that can collaborate towards achieving precise objectives in an adversarial dynamic environment. We have pursued this work in the context of robotic...
Multi-agent domains consisting of teams of agents that need to collaborate in an adversarial environment offer challenging research opportunities. In this article, we introduce periodic team...
Multi-agent domains consisting of teams of agents that need to collaborate in an adversarial environment offer challenging research opportunities. In this article, we introduce periodic team...
The CMUnited-98 champion small robot team (1999)
Manuela Veloso, Michael Bowling, Peter Stone
Robotic soccer presents a large spectrum of challenging research opportunities. In this article, we present the main research and technical contributions of our champion CMUnited-98 small robot team....
Task decomposition and dynamic role assignment for real-time strategic teamwork (1999)
Abstract. Multi-agent domains consisting of teams of agents that need to collaborate in an adversarial environment offer challenging research opportunities. In this paper, we introduce periodic team...
Multi-agent domains consisting of teams of agents that need to collaborate in an adversarial environment offer challenging research opportunities. In this article, we introduce periodic team...
Multi-agent domains consisting of teams of agents that need to collaborate in an adversarial environment offer challenging research opportunities. In this article, we introduce periodic team...
Layered Extrospection: Why is the agent doing what it’s doing (1999)
Peter Stone, Patrick Riley, Manuela Veloso
A perennial challenge in creating and using complex autonomous agents is following their choice of actions as the world changes dynamically and understanding why they act as they do. This paper...
Anticipation as a key for collaboration in a team of agents: A case study in robotic soccer (1999)
Manuela Veloso, Peter Stone, Michael Bowling
We investigate teams of complete autonomous agents that can collaborate towards achieving precise objectives in an adversarial dynamic environment. We have pursued this work in the context of robotic...
The CMUnited-99 Champion Simulator Team (1999)
Peter Stone, Patrick Riley, Manuela Veloso
The CMUnited-99 simulator team became the 1999 RoboCup simulator league champion by winning all 8 of its games, outscoring opponents by a combined score of 110--0. CMUnited-99 builds upon the...
CMUnited-98: RoboCup-98 Small-Robot World Champion Team (1999)
Manuela Veloso, Michael Bowling, Sorin Achim, Kwun Han, Peter Stone
The CMUnited small-robot team became the 1998 RoboCup small-robot league champion, repeating its 1997 victory. CMUnited-98 built upon the success of CMUnited-97, and involved a number of...
CMUnited-98: RoboCup-98 Simulator World Champion Team (1999)
Peter Stone, Manuela Veloso, Patrick Riley
The CMUnited-98 simulator team became the 1998 RoboCup simulator league champion by winning all 8 of its games, outscoring opponents by a total of 66--0. CMUnited-98 builds upon the successful...
Soccerserver Manual Ver. 5 Rev. 00 beta (for Soccerserver Ver.5.00 and later) (1999)
Ver Rev Beta, Klaus Dorer, Fredrik Heintz, Kostas Kostiadis, Johan Kummeneje, Helmut Myritz, ...
Contents 1 Introduction 3 1.1 History . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.1.1 History of the Soccerserver . . . . . . . . . . . . ....
The CMUnited-99 Champion Simulator Team (1999)
Peter Stone, Patrick Riley, Manuela Veloso
. The CMUnited-99 simulator team became the 1999 RoboCup simulator league champion by winning all 8 of its games, outscoring opponents by a combined score of 110--0. CMUnited-99 builds upon the...
The CMUnited-98 Champion Simulator Team (1999)
Peter Stone, Manuela Veloso, Patrick Riley
. The CMUnited-98 simulator team became the 1998 RoboCup simulator league champion by winning all 8 of its games, outscoring opponents by a total of 66--0. CMUnited-98 builds upon the successful...
Team-Partitioned, Opaque-Transition Reinforcement Learning (1999)
. We present a novel multi-agent learning paradigm called team-partitioned, opaque-transition reinforcement learning (TPOT-RL). TPOT-RL introduces the use of action-dependent features to generalize...
The CMUnited-98 Champion Simulator Team (1999)
Peter Stone, Manuela Veloso, Patrick Riley
. The CMUnited-98 simulator team became the 1998 RoboCup simulator league champion by winning all 8 of its games, outscoring opponents by a total of 66--0. CMUnited-98 builds upon the successful...
The CMUnited-98 Champion Small-Robot Team (1999)
Manuela Veloso, Michael Bowling, Sorin Achim, Kwun Han, Peter Stone
. In this chapter, we present the main research contributions of our champion CMUnited-98 small robot team. The team is a multiagent robotic system with global perception, and distributed cognition...
The CMUnited-98 champion simulator team (1999)
Peter Stone, Manuela Veloso, Patrick Riley, H. Kitano
Springer Verlag, Berlin, 1999. Abstract. The CMUnited-98 simulator team became the 1998 RoboCup simulator league champion by winning all 8 of its games, outscoring opponents by a total of 66{0....
The CMUnited-98 champion small robot team (1999)
Manuela Veloso, Michael Bowling, Sorin Achim, Kwun Han, Peter Stone
Abstract. In this chapter, we present the main research contributions of our champion CMUnited-98 small robot team. The team is a multiagent robotic system with global perception, and distributed...
CMUnited-98: Robocup-98 simulator world champion team (1999)
Peter Stone, Manuela Veloso, Patrick Riley
The CMUnited-98 simulator team became the 1998 RoboCup simulator league champion by winning all 8 of its games, outscoring opponents by a total of 66–0. CMUnited-98 builds upon the successful...
Task decomposition and dynamic role assignment for real-time strategic teamwork (1999)
Abstract. Multi-agent domains consisting of teams of agents that need to collaborate in an adversarial environment offer challenging research opportunities. In this paper, we introduce periodic team...
The CMUnited-98 champion small robot team (1999)
Manuela Veloso, Michael Bowling, Peter Stone
Robotic soccer presents a large spectrum of challenging research opportunities. In this article, we present the main research and technical contributions of our champion CMUnited-98 small robot team....
Multi-agent domains consisting of teams of agents that need to collaborate in an adversarial environment offer challenging research opportunities. In this article, we introduce periodic team...
Multi-agent domains consisting of teams of agents that need to collaborate in an adversarial environment offer challenging research opportunities. In this article, we introduce periodic team...
The CMUnited-98 champion simulator team (1999)
Peter Stone, Patrick Riley, Manuela Veloso
simulator league champion by winning all 8 of its games, outscoring opponents by a combined score of 110{0. CMUnited-99 builds upon the successful CMUnited-98 implementation, but also improves upon...
The CMUnited-98 champion simulator team (1999)
Peter Stone, Patrick Riley, Manuela Veloso
simulator league champion by winning all 8 of its games, outscoring opponents by a combined score of 110{0. CMUnited-99 builds upon the successful CMUnited-98 implementation, but also improves upon...
Team-partitioned, opaque-transition reinforcement learning (1999)
In this paper, we present a novel multi-agent learning paradigm called team-partitioned, opaque-transition reinforcement learning (TPOT-RL). TPOT-RL introduces the concept of using action-dependent...
Beating a Defender in Robotic Soccer: Memory-Based Learning of a Continuous Function, (1998)
Learning how to adjust to an opponent's position is critical to the success of having intelligent agents collaborating towards the achievement of specific tasks in unfriendly environments. This paper...
Multiagent Systems: A Survey from a Machine Learning Perspective (1998)
Distributed Artificial Intelligence (DAI) has existed as a subfield of AI for less than two decades. DAI is concerned with systems that consist of multiple independent entities that interact in a...
Towards collaborative and adversarial learning: A case study in robotic soccer (1998)
Soccer is a rich domain for the study of multiagent learning issues. Not only must the players learn low-level skills, but they must also learn to work together and to adapt to the behaviors of...
A layered approach to learning client behaviors in the robocup soccer server (1998)
In the past few years, Multiagent Systems (MAS) has emerged as an active subfield of Artificial Intelligence (AI). Because of the inherent complexity of MAS, there is much interest in using Machine...
Communication in domains with unreliable, single-channel, low-bandwidth communication (1998)
In most multiagent systems with communicating agents, the agents have the luxury of using reliable, multi-step negotiation protocols. They can do so primarily when communication is reliable and the...
Communication in domains with unreliable, single-channel, low-bandwidth communication (1998)
Abstract. In most multiagent systems with communicating agents, the agents have the luxury of using reliable, multi-step negotiation protocols. They can do so primarily when communication is reliable...
Using decision tree confidence factors for multiagent control (1998)
Abstract. Although Decision Trees are widely used for classification tasks, they are typically not used for agent control. This paper presents a novel technique for agent control in a complex...
Anticipation: A Key for Collaboration in a Team of Agents (1998)
Manuela Veloso, Peter Stone, Michael Bowling
We investigate teams of complete autonomous agents that can collaborate towards achieving precise objectives in an adversarial dynamic environment. We have pursued this work in the context of robotic...
Towards collaborative and adversarial learning: A case study in robotic soccer (1998)
Soccer is a rich domain for the study of multiagent learning issues. Not only must the players learn low-level skills, but they must also learn to work together and to adapt to the behaviors of...
The cmunited-97 robotic soccer team: Perception and multiagent control (1998)
Manuela Veloso, Peter Stone, Kwun Han
Submitted to Autonomous Agents ’98, October 1997 Robotic soccer is a challenging research domain which involves multiple agents that need to collaborate in an adversarial environment to achieve...
CMUnited-97: RoboCup-97 small-robot world champion team (1998)
Manuela Veloso, Peter Stone, Kwun Han
Robotic soccer is a challenging research domain which involves multiple agents that need to collaborate in an adversarial environment to achieve specific objectives. In this paper, we describe...
CMUnited-97: RoboCup-97 small-robot world champion team (1998)
Manuela Veloso, Peter Stone, Kwun Han, Sorin Achim
Robotic soccer is a challenging research domain which involves multiple agents that need to collaborate in an adversarial environment to achieve specific objectives. In this paper, we describe...
CMUnited: A team of robotic soccer agents collaborating in an adversarial environment (1998)
Manuela Veloso, Peter Stone, Kwun Han, Sorin Achim
Robotic soccer involves multiple agents that need to collaborate in an adversarial environment to achieve specific objectives. In this paper, we describe the robotic agents and architecture that we...
Anticipation: A Key for Collaboration in a Team of Agents (1998)
Manuela Veloso, Peter Stone, Michael Bowling
We investigate teams of complete autonomous agents that can collaborate towards achieving precise objectives in an adversarial dynamic environment. We have pursued this work in the context of robotic...
Towards collaborative and adversarial learning: A case study in robotic soccer (1998)
Soccer is a rich domain for the study of multiagent learning issues. Not only must the players learn low-level skills, but they must also learn to work together and to adapt to the behaviors of...
The CMUnited-97 Small Robot Team (1998)
Manuela Veloso, Peter Stone, Kwun Han, Sorin Achim
. Robotic soccer is a challenging research domain which involves multiple agents that need to collaborate in an adversarial environment to achieve specific objectives. In this paper, we describe...
The CMUnited-97 Robotic Soccer Team: Perception and Multiagent Control (1998)
Manuela Veloso, Peter Stone, Kwun Han
Robotic soccer is a challenging research domain which involves multiple agents that need to collaborate in an adversarial environment to achieve specific objectives. In this paper, we describe...
Using Decision Tree Confidence Factors for Multiagent Control (1998)
Although Decision Trees are widely used for classification tasks, they are typically not used for agent control. This paper presents a novel technique for agent control in a complex multiagent domain...
The RoboCup Physical Agent Challenge: Phase I (1998)
Minoru Asada, Peter Stone, Hiroaki Kitano, Barry Werger, Yasuo Kuniyoshi, Alexis Drogoul, ...
Traditional AI research has not given due attention to the important role that physical bodies play for agents as their interactions produce complex emergent behaviors to achieve goals in the dynamic...
Multi-agent domains consisting of teams of agents that need to collaborate in an adversarial environment offer challenging research opportunities. In this article, we introduce periodic team...
A Layered Approach to Learning Client Behaviors in the RoboCup Soccer Server (1998)
Peter Stone Manuela, Peter Stone, Manuela Veloso
In the past few years, Multiagent Systems (MAS) has emerged as an active subfield of Artificial Intelligence (AI). Because of the inherent complexity of MAS, there is much interest in using Machine...
Communication in Domains with Unreliable, Single-Channel, Low-Bandwidth Communication (1998)
In most multiagent systems with communicating agents, the agents have the luxury of using reliable, multi-step negotiation protocols. They can do so primarily when communication is reliable and the...
Task Decomposition and Dynamic Role Assignment for Real-Time Strategic Teamwork (1998)
. Multi-agent domains consisting of teams of agents that need to collaborate in an adversarial environment offer challenging research opportunities. In this paper, we introduce periodic team...
A Layered Approach to Learning Client Behaviors in the RoboCup Soccer Server (1998)
In the past few years, Multiagent Systems (MAS) has emerged as an active subfield of Artificial Intelligence (AI). Because of the inherent complexity of MAS, there is much interest in using Machine...
Task Decomposition and Dynamic Role Assignment for Real-Time Strategic Teamwork (1998)
Multiagent domains consisting of teams of agents that need to collaborate in an adversarial environment offer challenging research opportunities. In this paper, we introduce periodic team...
Using Decision Tree Confidence Factors for Multiagent Control (1998)
. Although Decision Trees are widely used for classification tasks, they are typically not used for agent control. This paper presents a novel technique for agent control in a complex multiagent...
Multi-agent domains consisting of teams of agents that need to collaborate in an adversarial environment offer challenging research opportunities. In this article, we introduce periodic team...
Individual and Collaborative Behaviors in a Team of Homogeneous Robotic Soccer Agents (1998)
Robotic soccer is a new challenging multi-agentdomain, in which agents need to collaborate in an adversarial environment to achieve specific objectives. In this paper, we describe CMUnited-97, our...
The CMUnited-97 Simulator Team (1998)
. The Soccer Server system provides a rich and challenging multiagent, real-time domain. Agents must accurately perceive and act despite a quickly changing, largely hidden, noisy world. They must...
Communication in Domains with Unreliable, Single-Channel, Low-Bandwidth Communication (1998)
. In most multiagent systems with communicating agents, the agents have the luxury of using reliable, multi-step negotiation protocols. They can do so primarily when communication is reliable and the...
CMUnited-97: RoboCup-97 Small-Robot World Champion Team (1998)
Manuela Veloso, Peter Stone, Kwun Han
Robotic soccer is a challenging research domain which involves multiple agents that need to collaborate in an adversarial environment to achieve specific objectives. In this paper, we describe...
Prediction, Behaviors, and Collaboration in a Team of Robotic Soccer Agents (1998)
Manuela Veloso, Peter Stone, Kwun Han, Sorin Achim
Robotic soccer is a challenging research domain which involves multiple agents that need to collaborate in an adversarial environment to achieve specific objectives. In this paper, we describe...
The CMUnited-97 Small Robot Team (1998)
Manuela Veloso, Peter Stone, Kwun Han, Sorin Achim
. Robotic soccer is a challenging research domain which involves multiple agents that need to collaborate in an adversarial environment to achieve specific objectives. In this paper, we describe...
Team-Partitioned, Opaque-Transition Reinforcement Learning (1998)
In this paper, we present a novel multi-agent learning paradigm called team-partitioned, opaque-transition reinforcement learning (TPOT-RL). TPOT-RL introduces the concept of using action-dependent...
The CMUnited-97 Robotic Soccer Team: Perception and Multiagent Control (1998)
Manuela Veloso, Peter Stone, Kwun Han
Robotic soccer is a challenging research domain which involves multiple agents that need to collaborate in an adversarial environment to achieve specific objectives. In this paper, we describe...
A Layered Approach to Learning Client Behaviors in the RoboCup Soccer Server (1998)
In the past few years, Multiagent Systems (MAS) has emerged as an active subfield of Artificial Intelligence (AI). Because of the inherent complexity of MAS, there is much interest in using Machine...
Using decision tree confidence factors for multiagent control (1998)
Although Decision Trees are widely used for classification tasks, they are typically not used for agent control. This paper presents anovel technique for agent control in a complex multiagent domain...
Anticipation: A Key for Collaboration in a Team of Agents (1998)
Manuela Veloso, Peter Stone, Michael Bowling
We investigate teams of complete autonomous agents that can collaborate towards achieving precise objectives in an adversarial dynamic environment. We have pursued this work in the context of robotic...
Layered Learning in Multi-Agent Systems (1998)
search, or the U.S. Government. Keywords: Multi-agent systems, machine learning, multi-agent learning, control learning, hierarchical learning, reinforcement learning, decision tree learning, neural...
Communication in domains with unreliable, single-channel, low-bandwidth communication (1998)
In most multiagent systems with communicating agents, the agents have the luxury of using reliable, multi-step negotiation protocols. They can do so primarily when communication is reliable and the...
The CMUnited-97 small-robot team (1998)
Manuela Veloso, Peter Stone, Kwun Han, Sorin Achim
Abstract. Robotic soccer is a challenging research domain which involves multiple agents that need to collaborate in an adversarial environment toachieve speci c objectives. In this paper, we...
The CMUnited-97 simulator team (1998)
Abstract. The Soccer Server system provides a rich and challenging multiagent, real-time domain. Agents must accurately perceive and act despite a quickly changing, largely hidden, noisy world. They...
Anticipation: A Key for Collaboration in a Team of Agents (1998)
Manuela Veloso, Peter Stone, Michael Bowling
We investigate teams of complete autonomous agents that can collaborate towards achieving precise objectives in an adversarial dynamic environment. We have pursued this work in the context of robotic...
A layered approach to learning client behaviors in the robocup soccer server (1998)
In the past few years, Multiagent Systems (MAS) has emerged as an active subfield of Artificial Intelligence (AI). Because of the inherent complexity of MAS, there is much interest in using Machine...
Towards Collaborative and Adversarial Learning: A Case Study in Robotic Soccer (1997)
Soccer is a rich domain for the study of multiagent learning issues. Not only must the players learn low-level skills, but they must also learn to work together and to adapt to the behaviors of...
Multiagent Systems: A Survey from a Machine Learning Perspective (1997)
Distributed Artificial Intelligence (DAI) has existed as a subfield of AI for less than two decades. DAI is concerned with systems that consist of multiple independent entities that interact in a...
The RoboCup Synthetic Agent Challenge 97 (1997)
Hiroaki Kitano, Milind Tambe, Peter Stone, Manuela Veloso, Silvia Coradeschi, Eiichi Osawa, ...
RoboCup Challenge offers a set of challenges for intelligent agent researchers using a friendly competition in a dynamic, real-time, multiagent domain. While RoboCup in general envisions longer range...
Multiagent Systems: A Survey from a Machine Learning Perspective (1997)
Distributed Artificial Intelligence (DAI) has existed as a subfield of AI for less than two decades. DAI is concerned with systems that consist of multiple independent entities that interact in a...
Towards Collaborative and Adversarial Learning: A Case Study in Robotic Soccer (1997)
Soccer is a rich domain for the study of multi-agent learning issues. Not only must the players learn to adapt to the behavior of different opponents, but they must learn to work together. We are...
The RoboCup Synthetic Agent Challenge 97 (1997)
Hiroaki Kitano, Milind Tambe, Peter Stone, Manuela Veloso, Silvia Coradeschi, Eiichi Osawa, ...
RoboCup Challenge offers a set of challenges for intelligent agent researchers using a friendly competition in a dynamic, real-time, multiagent domain. While RoboCup in general envisions longer range...
Towards Collaborative and Adversarial Learning: A Case Study in Robotic Soccer (1997)
Soccer is a rich domain for the study of multiagent learning issues. Not only must the players learn low-level skills, but they must also learn to work together and to adapt to the behaviors of...
Multiagent Systems: A Survey from a Machine Learning Perspective (1997)
Distributed Artificial Intelligence (DAI) has existed as a subfield of AI for less than two decades. DAI is concerned with systems that consist of multiple independent entities that interact in a...
The RoboCup Synthetic Agent Challenge 97 (1997)
Hiroaki Kitano, Milind Tambe, Peter Stone, Manuela Veloso, Silvia Coradeschi, Eiichi Osawa, ...
RoboCup Challenge offers a set of challenges for intelligent agent researchers using a friendly competition in a dynamic, real-time, multiagent domain. While RoboCup in general envisions longer range...
A Layered Approach to Learning Client Behaviors in the RoboCup Soccer Server (1997)
In the past few years, Multiagent Systems (MAS) has emerged as an active subfield of Artificial Intelligence (AI). Because of the inherent complexity of MAS, there is much interest in using Machine...
The RoboCup Synthetic Agent Challenge 97 (1997)
Hiroaki Kitano, Milind Tambe, Peter Stone, Manuela Veloso, Silvia Coradeschi, Eiichi Osawa, ...
RoboCup Challenge offers a set of challenges for intelligent agent researchers using a friendly competition in a dynamic, real-time, multiagent domain. While RoboCup in general envisions longer range...
A Layered Approach for an Autonomous Robotic Soccer System (1997)
Manuela Veloso, Peter Stone, Sorin Achim
This paper is a very brief introduction to our work and the reader is referred to the references provided. We present the architecture of the physical system and introduce how actions are layered...
The RoboCup synthetic agent challenge 97 (1997)
Hiroaki Kitano, Milind Tambe, Peter Stone, Manuela Veloso, Silvia Coradeschi, Eiichi Osawa, ...
RoboCup Challenge o ers a set of challenges for intelligent agent researchers using a friendly competition in a dynamic, real-time, multiagent domain. While RoboCup in general envisions longer range...
The RoboCup Physical Agent Challenge: Phase-I (1997)
Minoru Asada, Peter Stone, Hiroaki Kitano, Barry Werger, Yasuo Kuniyoshi, Alexis Drogoul, ...
Traditional AI research has not given due attention to the important role that physical bodies play for agents as their interactions produce complex emergent behaviors to achieve goals in the dynamic...
The RoboCup synthetic agent challenge 97 (1997)
Hiroaki Kitano, Milind Tambe, Peter Stone, Manuela Veloso, Silvia Coradeschi, Eiichi Osawa, ...
matsubarfletl.go.jp nodafletl.go.jp asadaflmech.eng.osaka-u.ac.jp RoboCup Challenge offers a set of challenges for intelligent agent researchers using a friendly competition in a dynamic, real-time,...
Predictive memory for an inaccessible environment (1996)
Mike Bowling, Peter Stone, Manuela Veloso
Inaccessible and nondeterministic environ-ments are very common in real-world problems. One of the difficulties in these environments is representing the knowledge about the unknown aspects of the...
Simon Shackley, James Risbey, Peter Stone, Brian Wynne
Abstract. This paper surveys and interprets the attitudes of scientists to the use of flux adjustments in climate projections with coupled Atmosphere Ocean General Circulation Models. The survey is...
Beating a Defender in Robotic Soccer: Memory-Based Learning of a Continuous Function (1996)
Peter Stone And, Peter Stone, Manuela Veloso
Learning how to adjust to an opponent's position is critical to the success of having intelligent agents collaborating towards the achievement of specific tasks in unfriendly environments. This...
Beating a Defender in Robotic Soccer: Memory-Based Learning of a Continuous Function (1996)
Learning how to adjust to an opponent's position is critical to the success of having intelligent agents collaborating towards the achievement of specific tasks in unfriendly environments. This...
Predictive Memory for an Inaccessible Environment (1996)
Mike Bowling, Peter Stone, Manuela Veloso
Inaccessible and nondeterministic environments are very common in real-world problems. One of the difficulties in these environments is representing the knowledge about the unknown aspects of the...
Predictive Memory for an Inaccessible Environment (1996)
Mike Bowling, Peter Stone, Manuela Veloso
Inaccessible and nondeterministic environments are very common in real-world problems. One of the difficulties in these environments is representing the knowledge about the unknown aspects of the...
Beating a Defender in Robotic Soccer: Memory-Based Learning of a Continuous Function (1996)
Learning how to adjust to an opponent's position is critical to the success of having intelligent agents collaborating towards the achievement of specific tasks in unfriendly environments. This...
Building a Dedicated Robotic Soccer System (1996)
Sorin Achim, Peter Stone, Manuela Veloso
Robotic Soccer involves multiple agents that need to collaborate in an adversarial environment to achieve specific objectives. We have been building an architecture that addresses this integration of...
Building a dedicated robotic soccer system (1996)
Sorin Achim, Peter Stone, Manuela Veloso
Robotic Soccer involves multiple agents that need to collaborate in an adversarial environment toachieve speci c objectives. We have been building an architecture that addresses this integration of...
Beating a defender in robotic soccer: Memory-based learning of a continuous function (1996)
Learning how to adjust to an opponent's position is critical to the success of having intelligent agents collaborating towards the achievement of speci c tasks in unfriendly environments. This...
User-guided interleaving of planning and execution (1996)
We explore two advantages of interleaving execution with planning. First, the overall planning and execution time can be reduced. Second, information from the environment can be incorporated into the...
Predictive memory for an inaccessible environment (1996)
Mike Bowling, Peter Stone, Manuela Veloso
Inaccessible and nondeterministic environments are very common in real-world problems. One of the di culties in these environments is representing the knowledge about the unknown aspects of the...
FLECS: Planning with a flexible commitment strategy (1995)
Manuela Veloso, Peter Stone, Velosocs Cmu. Edu
There has been evidence that least-commitment planners can efficiently handle planning problems that involve difficult goal interactions. This evidence has led to the common belief that...
User-Guided Interleaving of Planning and Execution (1995)
We explore two advantages of interleaving execution with planning. First, the overall planning and execution time can be reduced. Second, information from the environment can be incorporated into the...
FLECS: Planning with a Flexible Commitment Strategy (1995)
There has been evidence that least-commitment planners can efficiently handle planning problems that involve difficult goal interactions. This evidence has led to the common belief that...
User-Guided Interleaving of Planning and Execution (1995)
We explore two advantages of interleaving execution with planning. First, the overall planning and execution time can be reduced. Second, information from the environment can be incorporated into the...
User-Guided Interleaving of Planning and Execution (1995)
. We explore two advantages of interleaving execution with planning. First, the overall planning and execution time can be reduced. Second, information from the environment can be incorporated into...
Broad Learning from Narrow Training: A Case Study in Robotic Soccer (1995)
The range of unseen instances that can be successfully classified by a learning algorithm is determined not only by the distribution of the training data, but also by the parameters of the function...
There has been evidence that least-commitment planners can efficiently handle planning problems that involve difficult goal interactions. This evidence has led to the common belief that...
FLECS: Planning with a flexible commitment strategy (1995)
There has been evidence that least-commitment planners can e ciently handle planning problems that involve di cult goal interactions. This evidence has led to the common belief that...
FLECS: Planning with a flexible commitment strategy (1995)
There has been evidence that least-commitment planners can e ciently handle planning problems that involve di cult goal interactions. This evidence has led to the common belief that...
The need for different domain-independent heuristics (1994)
Peter Stone, Manuela Veloso, Jim Blythe
PRODIGY's planning lgorithm uses domain-independent search heuristics. In this paper, wc support our bclic that there is no single search heuristic that performs more efficiently than others for...
Learning to Solve Complex Planning Problems: Finding Useful Auxiliary Problems (1994)
Learning from past experience allows a problem solver to increase its solvability horizon from simple to complex problems. For planners, learning involves a training phase during which knowledge is...
The Need for Different Domain-Independent Heuristics (1994)
Peter Stone, Manuela Veloso, Jim Blythe
PRODIGY's planning algorithm uses domain-independent search heuristics. In this paper, we support our belief that there is no single search heuristic that performs more efficiently than others...
The Need for Different Domain-Independent Heuristics (1994)
Peter Stone, Manuela Veloso, Jim Blythe
prodigy's planning algorithm uses domain-independent search heuristics. In this paper, we support our belief that there is no single search heuristic that performs more e#ciently than others for...
The Need for Different Domain-Independent Heuristics (1994)
Peter Stone, Manuela Veloso, Jim Blythe
prodigy's planning algorithm uses domain-independent search heuristics. In this paper, we support our belief that there is no single search heuristic that performs more efficiently than others...
The Need for Different Domain-Independent Heuristics (1994)
Peter Stone, Manuela Veloso, Jim Blythe
prodigy's planning algorithm uses domain-independent search heuristics. In this paper, we support our belief that there is no single search heuristic that performs more efficiently than others...
The need for di erent domain-independent heuristics (1994)
Peter Stone, Manuela Veloso, Jim Blythe
prodigy's planning algorithm uses domain-indep endent search heuristics. In this paper, we support our belief that there is no single search heuristic that performs more e ciently than others...
The need for di erent domain-independent heuristics (1994)
Peter Stone, Manuela Veloso, Jim Blythe
prodigy's planning algorithm uses domain-independent search heuristics. In this paper, we support our belief that there is no single search heuristic that performs more e ciently than others for...
Keeney,R.L.&Raiffa,H.Decisions with Multiple Objectives: Preferences and Value Tradeoffs (1976)
Model-based approaches to reinforcement learning exhibit low sample complexity while learning nearly optimal policies, but they are generally restricted to finite domains. Meanwhile, function...
Keeney,R.L.&Raiffa,H.Decisions with Multiple Objectives: Preferences and Value Tradeoffs (1976)
Model-based approaches to reinforcement learning exhibit low sample complexity while learning nearly optimal policies, but they are generally restricted to finite domains. Meanwhile, function...
Keeney,R.L.&Raiffa,H.Decisions with Multiple Objectives: Preferences and Value Tradeoffs (1976)
Model-based approaches to reinforcement learning exhibit low sample complexity while learning nearly optimal policies, but they are generally restricted to finite domains. Meanwhile, function...
Fibrations, cofibrations and homotopy equivalences /--by Peter Stone. (1973)
Thesis (M. Sc.)--Memorial University of Newfoundland, 1973.
Donen, Stanley (realización), Stone, Peter (guión), Clark, James (montaje), Mancini, Henry (música), Hepburn, Audrey (actuación), ...
Donen, Stanley (realización), Stone, Peter (guión), Clark, James (montaje), Mancini, Henry (música), Hepburn, Audrey (actuación), ...
0-78002-880-5
Wheeler, Amanda, Zanobetti, Antonella, Gold, Diane R., Schwartz, Joel, Stone, Peter, Suh, Helen H.
Associations between concentrations of ambient fine particles [particulate matter < 2.5 μ m aerodynamic diameter (PM2.5)] and heart rate variability (HRV) have differed by study population. We...
Mclaughlin, Thomas J, Aupont, Onesky, Bambauer, Kara Z, Stone, Peter, Mullan, Mariquita G, Colagiovanni, Jane, ...
Wheeler, Amanda, Zanobetti, Antonella, Gold, Diane R., Schwartz, Joel, Stone, Peter, Suh, Helen H.
Associations between concentrations of ambient fine particles [particulate matter < 2.5 μ m aerodynamic diameter (PM2.5)] and heart rate variability (HRV) have differed by study population. We...