Real-time opponent learning in automated negotiation using recursive Bayesian filtering

作者：

Highlights：

• Investigated opponent modeling in automated negotiation.

• Fuzzifyed the stakeholders evaluation models based on weighted preference limits.

• Proposed a recursive learning approach to learn the parameters of these models.

• A probabilistic model is applied that uses the learned criteria to find a proposal.

摘要

•Investigated opponent modeling in automated negotiation.•Fuzzifyed the stakeholders evaluation models based on weighted preference limits.•Proposed a recursive learning approach to learn the parameters of these models.•A probabilistic model is applied that uses the learned criteria to find a proposal.

论文关键词：Automated negotiation,Opponent modeling,Recursive Bayesian filtering,Unscented particle filtering

论文评审过程：Received 13 November 2018, Revised 13 March 2019, Accepted 14 March 2019, Available online 14 March 2019, Version of Record 21 March 2019.

论文官网地址：https://doi.org/10.1016/j.eswa.2019.03.025