Strategic agents for multi-resource negotiation using learning automata and case-based reasoning

In electronic commerce markets, agents often should acquire multiple resources to fulfill a high-level task. In order to attain such resources they need to compete with each other. In multi-agent environments, in which competition is involved, negotiation would be an interaction between agents in order to reach an agreement on resource allocation and to be coordinated with each other. During recent years, many strategies have been used for negotiation; but, their performance and success are not the same in different conditions. This paper presents a method base on case-based reasoning method and learning automata for agent negotiations. In the proposed method, case-based reasoning method and learning automata are used for selecting an efficient seller and successful strategy, respectively. Results of the experiments indicated that the proposed method has caused an improvement in some performance measures such as success rate and expected utility.