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BLGAN: Bayesian learning and genetic algorithm for supporting negotiation with incomplete information.

Kwang Mong Sim1, Yuanyuan Guo, Benyun Shi

  • 1Department of Computer Science, Hong 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
|December 11, 2008
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Summary
This summary is machine-generated.

This study introduces BLGAN, a novel approach combining Bayesian learning and genetic algorithms for automated negotiation with incomplete information. BLGAN agents achieve higher utilities and better outcomes by learning opponent strategies.

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Area of Science:

  • Artificial Intelligence
  • Multi-Agent Systems
  • Computational Economics

Background:

  • Automated negotiation is crucial for resolving agent differences.
  • Complete information negotiation allows optimal strategy computation via reserve price (RP) and deadline.
  • Incomplete information presents challenges for optimal strategy determination.

Purpose of the Study:

  • To develop an optimal negotiation strategy for agents operating under incomplete information.
  • To enhance negotiation outcomes by integrating Bayesian learning (BL) and genetic algorithms (GA).
  • To improve agent performance in terms of utility and combined negotiation outcomes (CNOs).

Main Methods:

  • The proposed BLGAN framework combines BL for estimating opponent RP and deadline with GA for proposal generation.
  • BL estimates opponent parameters, enabling GA to focus its search space.
  • GA generates proposals adaptively, compensating for estimation errors.

Main Results:

  • BLGAN agents successfully reached agreements.
  • Agents using BLGAN achieved higher utilities and CNOs compared to agents using only GA.
  • BLGAN outperformed agents that learned only RP or no opponent information.

Conclusions:

  • The synergy of BL and GA in BLGAN effectively addresses incomplete information in automated negotiation.
  • BLGAN provides a robust method for agents to learn and adapt to opponent strategies.
  • This approach significantly improves negotiation efficiency and outcomes.