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Prediction of the opponent's preference in bilateral multi-issue negotiation through Bayesian learning

Chapter


Abstract


  • In multi-issue negotiation, agents’ preferences are extremely important factors for reaching mutual beneficial agreements. However, agents would usually keeping their preferences in secret in order to avoid be exploited by their opponents during a negotiation. Thus, preference modelling has become an important research direction in the area of agent-based negotiation. In this paper, a bilateral multi-issue negotiation approach is proposed to help both negotiation agents to maximise their utilities under a setting that the opponent agent’s preference is private information. In the proposed approach, Bayesian learning is employed to analyse the opponent’s historical offers and approximately predicate the opponent’s preference over negotiation issues. Besides, a counter-offer proposition algorithm is integrated in our approach to help agents to generate mutual beneficial offers based on the preference learning result. Also, the experimental results indicate the good performance of the proposed approach in aspects of utility gain and negotiation efficiency.

Publication Date


  • 2016

Citation


  • Zhang, J., Ren, F. & Zhang, M. (2016). Prediction of the opponent's preference in bilateral multi-issue negotiation through Bayesian learning. In N. Fukuta, T. Ito, M. Zhang, K. Fujita & V. Robu (Eds.), Recent advances in agent-based complex automated negotiation (pp. 3-20). Switzerland: Springer International Publishing.

International Standard Book Number (isbn) 13


  • 9783319303055

Scopus Eid


  • 2-s2.0-84962289274

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/6041

Book Title


  • Recent advances in agent-based complex automated negotiation

Has Global Citation Frequency


Start Page


  • 3

End Page


  • 20

Place Of Publication


  • Switzerland

Abstract


  • In multi-issue negotiation, agents’ preferences are extremely important factors for reaching mutual beneficial agreements. However, agents would usually keeping their preferences in secret in order to avoid be exploited by their opponents during a negotiation. Thus, preference modelling has become an important research direction in the area of agent-based negotiation. In this paper, a bilateral multi-issue negotiation approach is proposed to help both negotiation agents to maximise their utilities under a setting that the opponent agent’s preference is private information. In the proposed approach, Bayesian learning is employed to analyse the opponent’s historical offers and approximately predicate the opponent’s preference over negotiation issues. Besides, a counter-offer proposition algorithm is integrated in our approach to help agents to generate mutual beneficial offers based on the preference learning result. Also, the experimental results indicate the good performance of the proposed approach in aspects of utility gain and negotiation efficiency.

Publication Date


  • 2016

Citation


  • Zhang, J., Ren, F. & Zhang, M. (2016). Prediction of the opponent's preference in bilateral multi-issue negotiation through Bayesian learning. In N. Fukuta, T. Ito, M. Zhang, K. Fujita & V. Robu (Eds.), Recent advances in agent-based complex automated negotiation (pp. 3-20). Switzerland: Springer International Publishing.

International Standard Book Number (isbn) 13


  • 9783319303055

Scopus Eid


  • 2-s2.0-84962289274

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/6041

Book Title


  • Recent advances in agent-based complex automated negotiation

Has Global Citation Frequency


Start Page


  • 3

End Page


  • 20

Place Of Publication


  • Switzerland