# Technical Report CIS9704

 TR#: CIS9704 Class: CIS Title: Exploration and Adaptation in Multiagent Systems: A Model-based Approach Authors: David Carmel and Shaul Markovitch PDF CIS9704.pdf Abstract: Agents that operate in a {\em multi-agent system} can benefit significantly from adapting to other agents while interacting with them. This work presents a general architecture for a model-based learning strategy combined with an exploration strategy. This combination enables adaptive agents to learn models of their rivals and to explore their behavior for exploitation in future encounters. We report experimental results in the {\em Iterated Prisoner's Dilemma} domain, demonstrating the superiority of the model-based learning agent over non-adaptive agents and over reinforcement-learning agents. The Experimental results also show that exploration can improve the performance of a model-based agent significantly. Copyright The above paper is copyright by the Technion, Author(s), or others. Please contact the author(s) for more information

Remark: Any link to this technical report should be to this page (http://www.cs.technion.ac.il/users/wwwb/cgi-bin/tr-info.cgi/1997/CIS/CIS9704), rather than to the URL of the PDF files directly. The latter URLs may change without notice.