Learning and Adaption in Multi-Agent Systems: First International Workshop, LAMAS 2005, Utrecht, The Netherlands, July 25, 2005, Revised Selected Papers

✍ Scribed by Pieter Jan ’t Hoen, Karl Tuyls, Liviu Panait (auth.), Karl Tuyls, Pieter Jan’t Hoen, Katja Verbeeck, Sandip Sen (eds.)

• Publisher: Springer-Verlag Berlin Heidelberg
• Year: 2006
• Tongue: English
• Leaves: 225
• Series: Lecture Notes in Computer Science 3898
• Edition: 1
• Category: Library

✦ Synopsis

This book contains selected and revised papers of the International Workshop on Lea- ing and Adaptation in Multi-Agent Systems (LAMAS 2005), held at the AAMAS 2005 Conference in Utrecht, The Netherlands, July 26. An important aspect in multi-agent systems (MASs) is that the environment evolves over time, not only due to external environmental changes but also due to agent int- actions. For this reason it is important that an agent can learn, based on experience, and adapt its knowledge to make rational decisions and act in this changing environment autonomously. Machine learning techniques for single-agent frameworks are well established. Agents operate in uncertain environments and must be able to learn and act - tonomously. This task is, however, more complex when the agent interacts with other agents that have potentially different capabilities and goals. The single-agent case is structurally different from the multi-agent case due to the added dimension of dynamic interactions between the adaptive agents. Multi-agent learning, i.e., the ability of the agents to learn how to cooperate and compete, becomes crucial in many domains. Autonomous agents and multi-agent systems (AAMAS) is an emerging multi-disciplinary area encompassing computer science, software engineering, biology, as well as cognitive and social sciences. A t- oretical framework, in which rationality of learning and interacting agents can be - derstood, is still under development in MASs, although there have been promising ?rst results.

✦ Table of Contents

Front Matter....Pages -
An Overview of Cooperative and Competitive Multiagent Learning....Pages 1-46
Multi-robot Learning for Continuous Area Sweeping....Pages 47-70
Learning Automata as a Basis for Multi Agent Reinforcement Learning....Pages 71-85
Learning Pareto-optimal Solutions in 2x2 Conflict Games....Pages 86-99
Unifying Convergence and No-Regret in Multiagent Learning....Pages 100-114
Implicit Coordination in a Network of Social Drivers: The Role of Information in a Commuting Scenario....Pages 115-128
Multiagent Traffic Management: Opportunities for Multiagent Learning....Pages 129-138
Dealing with Errors in a Cooperative Multi-agent Learning System....Pages 139-154
The Success and Failure of Tag-Mediated Evolution of Cooperation....Pages 155-164
An Adaptive Approach for the Exploration-Exploitation Dilemma and Its Application to Economic Systems....Pages 165-176
Efficient Reward Functions for Adaptive Multi-rover Systems....Pages 177-191
Multi-agent Relational Reinforcement Learning....Pages 192-206
Multi-type ACO for Light Path Protection....Pages 207-215
Back Matter....Pages -

✦ Subjects

Artificial Intelligence (incl. Robotics); Computer Communication Networks