Coevolving Negotiation Strategies for P-S-Optimizing Agents
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In this paper, we consider the negotiation between two competitive agents that consider both time and cost criteria. Therefore, the negotiation agents are designed to not only optimize price utility but also be successful in optimizing (negotiation) speed utility. To this end, the objective of this work is to find effective strategies for the negotiation. The strategies are coevolved through an evolutionary learning process using two different evolutionary algorithms (EAs)—a genetic algorithm (GA) and an estimation of distribution algorithm (EDA). We present an empirical comparison of GA and EDA in coevolving negotiation strategies with different preference criteria in optimizing the price and (negotiation) speed. The experimental results show that both EAs are successful in finding good solutions with respect to both the price-optimizing (P-Optimizing) and the speed-optimizing (S-Optimizing) negotiation. However, both EAs are not effective in the negotiation for the concurrent optimization of the price and speed (P-S-Optimizing negotiation). This is because in some cases, the original fitness function cannot characterize the difference among P-Optimizing, S-Optimizing, and P-S-Optimizing solutions. Hence, this paper proposes a new fitness function that can better differentiate among the P-Optimizing, S-Optimizing, and P-S-Optimizing solutions. The experiments showed that the EAs using the proposed fitness function can coevolve effective strategies for the exact P-S-Optimizing negotiation.
KeywordsSoftware agent Price and negotiation speed concurrent optimizing negotiation Genetic algorithms Estimation of distribution algorithms
This work was supported by the Korea Research Foundation Grant funded by the Korean Government (MEST) (KRF-2009-220-D00092).
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