A Model of Emotional Intelligent Agent for Cooperative Goal Exploration

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• 作者：Takashi Kuremoto (20)
  Tetsuya Tsurusaki (20)
  Kunikazu Kobayashi (21)
  Shingo Mabu (20)
  Masanao Obayashi (20)
  
• 关键词：intelligent agent ; emotion model ; circumplex model ; reinforcement learning ; Q ; learning
• 刊名：Lecture Notes in Computer Science
• 出版年：2013
• 出版时间：2013
• 年：2013
• 卷：7995
• 期：1
• 页码：31-38
• 全文大小：334KB
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  3. Kobayashi, K., Kurano, T., Kuremoto, T., Obayashi, M.: Cooperative Behavior Acquisition in Multi-agent Reinforcement Learning System Using Attention Degree. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds.) ICONIP 2012, Part III. LNCS, vol.?7665, pp. 537-44. Springer, Heidelberg (2012) CrossRef
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  6. Kuremoto, T., Obayashi, M., Kobayashi, K., Feng, L.-B.: Autonomic Behaviors of Swarm Robots Driven by Emotion and Curiosity. In: Li, K., Jia, L., Sun, X., Fei, M., Irwin, G.W. (eds.) LSMS 2010 and ICSEE 2010. LNCS (LNBI), vol.?6330, pp. 541-47. Springer, Heidelberg (2010) CrossRef
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  8. Watada, S., Obayashi, M., Kuremoto, T., Kobayashi, K.: A New Decision-Making System of an Agent Based on Emotional Models in Multi-Agent System. In: Proceedings of 18th International Symposium on Artificial Life and Robotics (ARBO 2013), pp. 452-55 (2013)
  9. Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. The MIT Press (1998)
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  12. Doya, K.: Metalearning and Neuromodulation. Neural Networks?15(4), 495-06 (2002) CrossRef
  
• 作者单位：Takashi Kuremoto (20)
  Tetsuya Tsurusaki (20)
  Kunikazu Kobayashi (21)
  Shingo Mabu (20)
  Masanao Obayashi (20)
  
  20. Graduate School of Science and Engineering, Yamaguchi University, Tokiwadai 2-16-1, Ube, Yamaguchi, 755-8611, Japan
  21. School of Information Science & Technology, Aichi Prefectural University, Ibaragabasama 1522-3, Nagakute, Aichi, 480-1198, Japan
  

文摘

In this study, a novel model of the intelligent agent is proposed by introducing a dynamic emotion model into conventional action selection policy of the reinforcement learning method. Comparing with the conventional Q-learning of reinforcement learning, the proposed method adds two emotional factors in to the state-action value function: “arousal value-factor which affects motivation of action and “pleasure value-factor which influences the probability of action selection. The emotional factors are affected by the other agents when multiple agents exist in the perception area. Computer simulations of pursuit problems of static/dynamic preys were performed and all results showed effectiveness of the proposed method, i.e., faster learning convergence was confirmed comparing with the case of conventional Q-learning method.