Transfer for Automated Negotiation

AbstractLearning in automated negotiation is a difficult problem because the target function is hidden and the available experience for learning is rather limited. Transfer learning is a branch of machine learning research concerned with the reuse of previously acquired knowledge in new learning tasks, for example, in order to reduce the amount of learning experience required to attain a certain level of performance. This paper proposes a novel strategy based on a variation of TrAdaBoost—a classic instance transfer technique—that can be used in a multi-issue negotiation setting. The experimental results show that the proposed method is effective in a variety of application domains against the state-of-the-art negotiating agents.

[1]  A. Rubinstein Perfect Equilibrium in a Bargaining Model , 1982 .

[2]  Sarit Kraus,et al.  Genius: negotiation environment for heterogeneous agents , 2009, AAMAS.

[3]  Kuldeep Kumar,et al.  Agent-based negotiation and decision making for dynamic supply chain formation , 2009, Eng. Appl. Artif. Intell..

[4]  Luc De Raedt,et al.  Proceedings of the 20th European Conference on Artificial Intelligence , 2012 .

[5]  Ho-fung Leung,et al.  ABiNeS: An Adaptive Bilateral Negotiating Strategy over Multiple Items , 2012, 2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology.

[6]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[7]  Ilja Ponka Commitment models and concurrent bilateral negotiation strategies in dynamic service markets , 2009 .

[8]  Raymond Y. K. Lau,et al.  Knowledge discovery for adaptive negotiation agents in e-marketplaces , 2008, Decis. Support Syst..

[9]  Peter Stone,et al.  Transfer Learning for Reinforcement Learning Domains: A Survey , 2009, J. Mach. Learn. Res..

[10]  Haitham Bou-Ammar,et al.  Reinforcement learning transfer via sparse coding , 2012, AAMAS.

[11]  Gerhard Weiss,et al.  Optimizing complex automated negotiation using sparse pseudo-input gaussian processes , 2013, AAMAS.

[12]  Qiang Yang,et al.  Boosting for transfer learning , 2007, ICML '07.

[13]  Sung-Bong Yang,et al.  An efficient multilateral negotiation system for pervasive computing environments , 2008, Eng. Appl. Artif. Intell..

[14]  Nicholas R. Jennings,et al.  Learning on opponent's preferences to make effective multi-issue negotiation trade-offs , 2004, ICEC '04.

[15]  N. R. Jennings,et al.  To appear in: Int Journal of Group Decision and Negotiation GDN2000 Keynote Paper Automated Negotiation: Prospects, Methods and Challenges , 2022 .

[16]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[17]  Gerhard Weiss,et al.  An Efficient and Adaptive Approach to Negotiation in Complex Environments , 2012, ECAI.

[18]  Jacques L. Koko,et al.  The Art and Science of Negotiation , 2009 .

[19]  Gerhard Weiss,et al.  An efficient automated negotiation strategy for complex environments , 2013, Eng. Appl. Artif. Intell..