Modeling Coalition Formation for Repeated Games using Learning Approaches
In this paper, we introduce the notion of weight to tasks capability, and describe the use of case-based learning and reinforcement learning in a coalition formation model when games are repeated. Based on the the notion weight we introduce, a weight-based coalition formation algorithm is proposed, but this algorithm cant always generate good coalitions, to supplement this, an randomized weight-based coalition formation algorithm is introduced. However, deciding when to use which algorithm is not such an easy thing, so a notion of degree of similarity is defined, through learning, an optimal degree of similarity can be attained to solve the above problem. In a word, we handle the coalition formation problem in a more of machine learning and data driven perspective.
coalition formation learning approaches
Zhong-Cun Wang Chong-Jun Wang
National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University,Nanjing 210093, China
国际会议
太原
英文
145-149
2010-10-22（万方平台首次上网日期，不代表论文的发表时间）