Adaptive Agents and Multi-Agent Systems

Cooperation and learning are two ways in which an agent can improve its performance. Cooperative Multiagent Learning is a framework to analyze the tradeoff between cooperation and learning in multiagent systems. We focus on multiagent systems where individual agents are capable of solving problems and learning using CBR (Case-based Reasoning). We present several collaboration strategies for agents that learn and their empirical results in several experiments. Finally we analyze the collaboration strategies and their results along several dimensions, like number of agents, redundancy, CBR technique used, and individual decision policies.

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