Rules of encounter: designing conventions for automated negotiation among computers
Rules of encounter: designing conventions for automated negotiation among computers
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Machine Learning
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Genetic Algorithms in Search, Optimization and Machine Learning
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
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Computational Optimization and Applications
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PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
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ICMAS '98 Proceedings of the 3rd International Conference on Multi Agent Systems
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CCGRID '02 Proceedings of the 2nd IEEE/ACM International Symposium on Cluster Computing and the Grid
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In this paper, we present an estimation of distribution algorithm (EDA) augmented with enhanced dynamic diversity controlling and local improvement methods to solve competitive coevolution problems for agent-based automated negotiations. Since optimal negotiation strategies ensure that interacting agents negotiate optimally, finding such strategies--particularly, for the agents having incomplete information about their opponents--is an important and challenging issue to support agent-based automated negotiation systems. To address this issue, we consider the problem of finding optimal negotiation strategies for a bilateral negotiation between self-interested agents with incomplete information through an EDA-based coevolution mechanism. Due to the competitive nature of the agents, EDAs should be able to deal with competitive coevolution based on two asymmetric populations each consisting of self-interested agents. However, finding optimal negotiation solutions via coevolutionary learning using conventional EDAs is difficult because the EDAs suffer from premature convergence and their search capability deteriorates during coevolution. To solve these problems, even though we have previously devised the dynamic diversity controlling EDA (D2C-EDA), which is mainly characterized by a diversification and refinement (DR) procedure, D2C-EDA suffers from the population reinitialization problem that leads to a computational overhead. To reduce the computational overhead and to achieve further improvements in terms of solution accuracy, we have devised an improved D2C-EDA (ID2C-EDA) by adopting an enhanced DR procedure and a local neighborhood search (LNS) method. Favorable empirical results support the effectiveness of the proposed ID2C-EDA compared to conventional and the other proposed EDAs. Furthermore, ID2C-EDA finds solutions very close to the optimum.