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Evolving fuzzy rules for relaxed-criteria negotiation
1Hong Kong Baptist University, Kowloon Tong, Hong Kong. prof_sim_2002@yahoo.com
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.
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.
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