Self-organizing neural models integrating rules and reinforcement learning

• Main Authors: TENG, Teck-Hou, TAN, Zhong-Ming, TAN, Ah-hwee
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2008
• Subjects: self-organizing neural model; reinforcement learning; supervised learning; fusion architecture; cognition; knowledge refinement; symbolic rule; temporal-difference learning method; Databases and Information Systems; OS and Networks
• Online Access: https://ink.library.smu.edu.sg/sis_research/6556; https://ink.library.smu.edu.sg/context/sis_research/article/7559/viewcontent/Integrating_Rules_IJCNN08_av.pdf
• Institution: Singapore Management University

Traditional approaches to integrating knowledge into neural network are concerned mainly about supervised learning. This paper presents how a family of self-organizing neural models known as fusion architecture for learning, cognition and navigation (FALCON) can incorporate a priori knowledge and pe...