Strategic agents for Multi-Resource Negotiation Using Learning Automata and Case-Based Reasoning

被引:0
作者
Haghighatjoo, Monireh [1 ]
Masoumi, Behrooz [1 ]
Meybodi, Mohamad Reza [2 ]
机构
[1] Islamic Azad Univ, Fac Comp & Informat Technol, Qazvin Branch, Qazvin, Iran
[2] Amirkabir Univ, Dept Comp Engn & Informat Technol, Tehran, Iran
来源
2014 4TH INTERNATIONAL CONFERENCE ON COMPUTER AND KNOWLEDGE ENGINEERING (ICCKE) | 2014年
关键词
multi agent system; resource allocation; negotiation; learning autoiruda; case-based reasoning;
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
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.
引用
收藏
页码:35 / 41
页数:7
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