IMPROVING AGENT PERFORMANCE FOR MULTI-RESOURCE NEGOTIATION USING LEARNING AUTOMATA AND CASE-BASED REASONING

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HAGHIGHATJOO MONIREH; MASOUMI BEHROOZ; MEYBODI MOHAMAD REZA

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Journal Paper

Paper Information

Journal: JOURNAL OF COMPUTER AND ROBOTICS Year:2014 | Volume:7 | Issue:2 Page(s): 55-62

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Title

IMPROVING AGENT PERFORMANCE FOR MULTI-RESOURCE NEGOTIATION USING LEARNING AUTOMATA AND CASE-BASED REASONING

Author(s)

HAGHIGHATJOO MONIREH | MASOUMI BEHROOZ | MEYBODI MOHAMAD REZA | Issue Writer Certificate

Keywords

MULTI AGENT SYSTEM

RESOURCE ALLOCATION

NEGOTIATION

LEARNING AUTOMATA

CASE -BASED REASONING

Abstract

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.

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APA: Copy

HAGHIGHATJOO, MONIREH, MASOUMI, BEHROOZ, & MEYBODI, MOHAMAD REZA. (2014). IMPROVING AGENT PERFORMANCE FOR MULTI-RESOURCE NEGOTIATION USING LEARNING AUTOMATA AND CASE-BASED REASONING. JOURNAL OF COMPUTER AND ROBOTICS, 7(2), 55-62. SID. https://sid.ir/paper/644533/en

Vancouver: Copy

HAGHIGHATJOO MONIREH, MASOUMI BEHROOZ, MEYBODI MOHAMAD REZA. IMPROVING AGENT PERFORMANCE FOR MULTI-RESOURCE NEGOTIATION USING LEARNING AUTOMATA AND CASE-BASED REASONING. JOURNAL OF COMPUTER AND ROBOTICS[Internet]. 2014;7(2):55-62. Available from: https://sid.ir/paper/644533/en

IEEE: Copy

MONIREH HAGHIGHATJOO, BEHROOZ MASOUMI, and MOHAMAD REZA MEYBODI, “IMPROVING AGENT PERFORMANCE FOR MULTI-RESOURCE NEGOTIATION USING LEARNING AUTOMATA AND CASE-BASED REASONING,” JOURNAL OF COMPUTER AND ROBOTICS, vol. 7, no. 2, pp. 55–62, 2014, [Online]. Available: https://sid.ir/paper/644533/en

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