Measuring the emotional state among interacting agents: A game theory approach using reinforcement learning
作者：
Highlights：
• We suggest a new method for measuring the emotional state among interacting agents.
• We employ a non-cooperative game theory approach for represent the interaction.
• The Reinforcement Learning process introduces the stimuli to the environment.
• For measuring the emotional state it is employed the Kullback–Leibler distance.
• We present an application example related to assessment centers.
摘要
•We suggest a new method for measuring the emotional state among interacting agents.•We employ a non-cooperative game theory approach for represent the interaction.•The Reinforcement Learning process introduces the stimuli to the environment.•For measuring the emotional state it is employed the Kullback–Leibler distance.•We present an application example related to assessment centers.
论文关键词：Adaptive autonomous agents,Emotional model,Kullback–Leibler distance,Game theory,Reinforcement learning
论文评审过程：Received 8 September 2017, Revised 18 December 2017, Accepted 19 December 2017, Available online 20 December 2017, Version of Record 26 December 2017.
论文官网地址：https://doi.org/10.1016/j.eswa.2017.12.036
