Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | Intelligent Agents and Multi-Agent Systems |
| Editors | Jaeho Lee, Mike Barley |
| Publisher | Springer Verlag |
| Pages | 50-61 |
| Number of pages | 12 |
| ISBN (Electronic) | 9783540204602 |
| Publication status | Published - 2003 Jan 1 |
| Event | 6th Pacific Rim International Workshop on Multi-Agents, PRIMA 2003 - Seoul, Korea, Republic of Duration: 2003 Nov 7 → 2003 Nov 8 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 2891 |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Other
| Other | 6th Pacific Rim International Workshop on Multi-Agents, PRIMA 2003 |
|---|---|
| Country | Korea, Republic of |
| City | Seoul |
| Period | 03/11/7 → 03/11/8 |
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All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)
Cite this
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Evolutionary learning of multiagents using strategic coalition in the IPD game. / Yang, Seung Ryong; Cho, Sung Bae.
Intelligent Agents and Multi-Agent Systems. ed. / Jaeho Lee; Mike Barley. Springer Verlag, 2003. p. 50-61 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2891).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Evolutionary learning of multiagents using strategic coalition in the IPD game
AU - Yang, Seung Ryong
AU - Cho, Sung Bae
PY - 2003/1/1
Y1 - 2003/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=9444223355&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=9444223355&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:9444223355
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 50
EP - 61
BT - Intelligent Agents and Multi-Agent Systems
A2 - Lee, Jaeho
A2 - Barley, Mike
PB - Springer Verlag
ER -