Social and economic systems consist of complex interactions among its members. Their behaviors become adaptive according to changing environment. In many cases, an individual's behaviors can be modeled by a stimulus-response system in a dynamic environment. In this paper, we use the Iterated Prisoner's Dilemma (IPD) game, which is a simple model to deal with complex problems for dynamic systems. We propose strategic coalition consisting of many agents and simulate their emergence in a co-evolutionary learning environment. Also we introduce the concept of confidence for agents in a coalition and show how such confidences help to improve the generalization ability of the whole coalition. Experimental results show that co-evolutionary learning with coalitions and confidence can produce better performing strategies that generalize well in dynamic environments.
|Title of host publication||Intelligent Agents and Multi-Agent Systems|
|Editors||Jaeho Lee, Mike Barley|
|Number of pages||12|
|Publication status||Published - 2003|
|Event||6th Pacific Rim International Workshop on Multi-Agents, PRIMA 2003 - Seoul, Korea, Republic of|
Duration: 2003 Nov 7 → 2003 Nov 8
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Other||6th Pacific Rim International Workshop on Multi-Agents, PRIMA 2003|
|Country/Territory||Korea, Republic of|
|Period||03/11/7 → 03/11/8|
Bibliographical notePublisher Copyright:
© Springer-Verlag Berlin Heidelberg 2003.
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)