Abstract
Humans learn from incidents in their own life and reflects these in subsequent actions as their own experiences. These experiences are memorized in the brain and recollected when necessary. This research incorporates this type of intelligent information processing mechanism and applies it to an autonomous agent. In the proposed system, the reinforcement Q-learning method is used. Autoassociative chaotic neural networks are also used as mutual associative memory systems. However, an agent cannot retrieve all stored patterns exactly, especially in the case of too many stored patterns and a strong correlation among them. To solve this problem, we propose to use types of attentive parameters and attentive characteristic patterns. The attentive characteristic pattern is part of the stored patterns. When robots concentrate their attention on a specific part of a stored pattern, i.e., the attentive characteristic pattern, whole stored patterns are retrieved easily and completely. Finally, the effectiveness of the proposed method is verified through a simulation applied to plural maze-searching problems.
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References
Obayashi M, Narita K, Kuremoto T, et al (2008) A reinforcement learning system with chaotic neural networks-based adaptive hierarchical memory structure for autonomous robots. Proceeding of an International Conference on Control, Automation, and Systems, pp 69–74
Sutton RS, Barto AG (1998) Reinforcement learning. MIT Press, Cambridge
Aihara K, Takabe T, Toyoda M (1990) Chaotic neural networks. Phys Lett A 14(6–7):333–340
Adachi K, Tanabe T (1997) Associative dynamics in chaotic neural networks. Neural Networks 10:83–98
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This work was presented in part at the 15th International Symposium on Artificial Life and Robotics, Oita, Japan, February 4–6, 2010
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Obayashi, M., Feng, LB., Kuremoto, T. et al. Intelligent agent construction using the attentive characteristic patterns of chaotic neural networks. Artif Life Robotics 15, 216–220 (2010). https://doi.org/10.1007/s10015-010-0796-5
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DOI: https://doi.org/10.1007/s10015-010-0796-5