TY  - BOOK
AU  - Tuyls,Karl
AU  - Hoen,Pieter Jan’t.
AU  - Verbeeck,Katja
AU  - Sen,Sandip
ED  - SpringerLink (Online service)
TI  - Learning and Adaption in Multi-Agent Systems: First International Workshop, LAMAS 2005, Utrecht, The Netherlands, July 25, 2005, Revised Selected Papers 
T2  - Lecture Notes in Computer Science,
SN  - 9783540330592
AV  - Q334-342 
U1  - 006.3 23
PY  - 2006///
CY  - Berlin, Heidelberg
PB  - Springer Berlin Heidelberg
KW  - Computer science
KW  - Computer Communication Networks
KW  - Artificial intelligence
KW  - Computer Science
KW  - Artificial Intelligence (incl. Robotics)
N1  - An Overview of Cooperative and Competitive Multiagent Learning -- Multi-robot Learning for Continuous Area Sweeping -- Learning Automata as a Basis for Multi Agent Reinforcement Learning -- Learning Pareto-optimal Solutions in 2x2 Conflict Games -- Unifying Convergence and No-Regret in Multiagent Learning -- Implicit Coordination in a Network of Social Drivers: The Role of Information in a Commuting Scenario -- Multiagent Traffic Management: Opportunities for Multiagent Learning -- Dealing with Errors in a Cooperative Multi-agent Learning System -- The Success and Failure of Tag-Mediated Evolution of Cooperation -- An Adaptive Approach for the Exploration-Exploitation Dilemma and Its Application to Economic Systems -- Efficient Reward Functions for Adaptive Multi-rover Systems -- Multi-agent Relational Reinforcement Learning -- Multi-type ACO for Light Path Protection
N2  - 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
UR  - http://dx.doi.org/10.1007/11691839
ER  -