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Evolving fuzzy rules for relaxed-criteria negotiation.

Kwang Mong Sim¹

¹Hong Kong Baptist University, Kowloon Tong, Hong Kong. prof_sim_2002@yahoo.com

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|November 22, 2008

Summary

Automated negotiation agents can now learn and adapt. Evolving market-driven negotiation agents (MDAs) with fuzzy rules outperform manually set ones, improving outcomes with experience.

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Area of Science:

Artificial Intelligence
Computational Economics
Multi-Agent Systems

Background:

Existing automated negotiation agents lack flexibility in relaxing criteria for faster consensus.
Current relaxed-criteria agents cannot evolve their rules through learning.
This limits their adaptability and performance improvement in dynamic e-markets.

Purpose of the Study:

To design market-driven negotiation agents (MDAs) with flexible, fuzzy-rule-based relaxed criteria.
To enable MDAs to evolve their relaxed-criteria fuzzy rules through learning.
To enhance negotiation outcomes by adapting to multiple e-market environments.

Main Methods:

Developed an evolutionary algorithm for adapting and evolving relaxed-criteria fuzzy rules.
Implemented market-driven negotiation agents (MDAs) with evolved (EvEMDAs) and manually constructed (EMDAs) rules.
Conducted stochastic simulations in a testbed to compare EvEMDAs and EMDAs across various e-markets.

Main Results:

EvEMDAs consistently outperformed EMDAs in diverse e-market settings.
The negotiation performance of EvEMDAs improved with increased participation in e-markets.
Evolutionary adaptation of fuzzy rules leads to superior negotiation outcomes.

Conclusions:

Evolving market-driven negotiation agents (EvEMDAs) demonstrate superior performance over static agents (EMDAs).
Adaptive learning of fuzzy rules enhances negotiation efficiency and certainty.
The proposed evolutionary approach enables agents to improve negotiation strategies over time.