This paper introduces a new negotiating agent model for automated negotiation. We focus on applications without time pressure with multidimensional negotiation on both continuous and discrete domains. The agent bidding strategy relies on Monte Carlo Tree Search, which is a trendy method since it has been used with success on games with high branching factor such as Go. It also exploits opponent modeling techniques thanks to Gaussian process regression and Bayesian learning. Evaluation is done by confronting the existing agents that are able to negotiate in such context: Random Walker, Tit-for-tat and Nice Tit-for-Tat. None of those agents succeeds in beating our agent. Also, the modular and adaptive nature of our approach is a huge advantage when it comes to optimize it in specific applicative contexts.
CITATION STYLE
Buron, C. L. R., Guessoum, Z., & Ductor, S. (2019). MCTS-Based Automated Negotiation Agent. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11873 LNAI, pp. 186–201). Springer. https://doi.org/10.1007/978-3-030-33792-6_12
Mendeley helps you to discover research relevant for your work.