Evolving Fuzzy Rules for Relaxed-Criteria Negotiation

Sim, Kwang Mong (2008) Evolving Fuzzy Rules for Relaxed-Criteria Negotiation. IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics, 38 (6). 1486 -1500 . ISSN 1083-4419 . (doi:10.1109/TSMCB.2008.928210) (Full text available)

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In the literature on automated negotiation, very few negotiation agents are designed with the flexibility to slightly relax their negotiation criteria to reach a consensus more rapidly and with more certainty. Furthermore, these relaxed-criteria negotiation agents were not equipped with the ability to enhance their performance by learning and evolving their relaxed-criteria negotiation rules. The impetus of this work is designing market-driven negotiation agents (MDAs) that not only have the flexibility of relaxing bargaining criteria using fuzzy rules, but can also evolve their structures by learning new relaxed-criteria fuzzy rules to improve their negotiation outcomes as they participate in negotiations in more e-markets. To this end, an evolutionary algorithm for adapting and evolving relaxed-criteria fuzzy rules was developed. Implementing the idea in a testbed, two kinds of experiments for evaluating and comparing EvEMDAs (MDAs with relaxed-criteria rules that are evolved using the evolutionary algorithm) and EMDAs (MDAs with relaxed-criteria rules that are manually constructed) were carried out through stochastic simulations. Empirical results show that: 1) EvEMDAs generally outperformed EMDAs in different types of e-markets and 2) the negotiation outcomes of EvEMDAs generally improved as they negotiated in more e-markets.

Item Type: Article
Uncontrolled keywords: Adaptive agent , automated negotiation , evolutionary algorithm , evolutionary computational economics , fuzzy decision controller (FDC) , intelligent agent , negotiation agent
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science
Divisions: Faculties > Sciences > School of Computing > Data Science
Depositing User: Kwang Mong Sim
Date Deposited: 24 Oct 2012 10:21 UTC
Last Modified: 08 Jun 2016 08:02 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/31928 (The current URI for this page, for reference purposes)
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