Abstract
In a dynamic system, such as social and economic systems, complex interactions emerge among its members. In that case, their behaviors become adaptive according to changing environment. In this paper, we use the iterated prisoner's dilemma (IPD) game, which is simple yet capable of dealing with complex problems, to model the dynamic system, and propose strategic coalition to obtain superior adaptive agents and simulate its emergence in a co-evolutionary learning environment. Also, we introduce the concept of confidence for agents in a coalition and show how such confidence helps improving the generalization ability of the evolved agents using strategic coalition. Experimental results show that co-evolutionary learning with coalition and confidence can produce better performing agents that generalize well against unseen agents.
| Original language | English |
|---|---|
| Pages (from-to) | 193-203 |
| Number of pages | 11 |
| Journal | Applied Soft Computing Journal |
| Volume | 5 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 2005 Jan 1 |
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All Science Journal Classification (ASJC) codes
- Software
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Co-evolutionary learning with strategic coalition for multiagents. / Yang, Seung Ryong; Cho, Sung-Bae.
In: Applied Soft Computing Journal, Vol. 5, No. 2, 01.01.2005, p. 193-203.Research output: Contribution to journal › Article
TY - JOUR
T1 - Co-evolutionary learning with strategic coalition for multiagents
AU - Yang, Seung Ryong
AU - Cho, Sung-Bae
PY - 2005/1/1
Y1 - 2005/1/1
N2 - In a dynamic system, such as social and economic systems, complex interactions emerge among its members. In that case, their behaviors become adaptive according to changing environment. In this paper, we use the iterated prisoner's dilemma (IPD) game, which is simple yet capable of dealing with complex problems, to model the dynamic system, and propose strategic coalition to obtain superior adaptive agents and simulate its emergence in a co-evolutionary learning environment. Also, we introduce the concept of confidence for agents in a coalition and show how such confidence helps improving the generalization ability of the evolved agents using strategic coalition. Experimental results show that co-evolutionary learning with coalition and confidence can produce better performing agents that generalize well against unseen agents.
AB - In a dynamic system, such as social and economic systems, complex interactions emerge among its members. In that case, their behaviors become adaptive according to changing environment. In this paper, we use the iterated prisoner's dilemma (IPD) game, which is simple yet capable of dealing with complex problems, to model the dynamic system, and propose strategic coalition to obtain superior adaptive agents and simulate its emergence in a co-evolutionary learning environment. Also, we introduce the concept of confidence for agents in a coalition and show how such confidence helps improving the generalization ability of the evolved agents using strategic coalition. Experimental results show that co-evolutionary learning with coalition and confidence can produce better performing agents that generalize well against unseen agents.
UR - http://www.scopus.com/inward/record.url?scp=10844252109&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=10844252109&partnerID=8YFLogxK
U2 - 10.1016/j.asoc.2004.07.002
DO - 10.1016/j.asoc.2004.07.002
M3 - Article
AN - SCOPUS:10844252109
VL - 5
SP - 193
EP - 203
JO - Applied Soft Computing
JF - Applied Soft Computing
SN - 1568-4946
IS - 2
ER -