Improving Agent Performance for Multi-Resource Negotiation Using Learning Automata and Case-Based Reasoning

Journal of Computer & Robotics

مقاله 8، دوره 7، شماره 2، مهر 2014، صفحه 55-62   

نویسندگان

Monireh Haghighatjoo^{* 1}؛ Behrooz Masoumi¹؛ Mohamad Reza Meybodi²

¹Faculty of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran

²Department of Computer Engineering and Information Technology, Amirkabir University, Tehran, Iran

چکیده

In electronic commerce markets, agents often should acquire multiple resources to fulfil 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. In recent years, negotiation has been employed to allocate resources in multi-agent systems. Yet, in most of the conventional methods, negotiation is done without considering past experiments. In this paper, in order to use experiments of agents, a hybrid method is used which employed case-based reasoning and learning automata in negotiation. In the proposed method, the buyer agent would determine its seller and its offered price based on the passed experiments and then an offer would be made. Afterwards, the seller would choose one of the allowed actions using learning automata. Results of the experiments indicated that the proposed algorithm has caused an improvement in some performance measures such as success rate.

کلیدواژه‌ها

Multi agent system؛ Resource allocation؛ Negotiation؛ Learning Automata؛ Case -based reasoning