An online incremental learning pattern-based reasoning system

Authors:
Shen Furao;Akihito Sudo;Osamu Hasegawa
Affiliations:
The State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210093, PR China;Imaging Science and Engineering Lab., Tokyo Institute of Technology, Japan;Imaging Science and Engineering Lab., Tokyo Institute of Technology, Japan
Venue:
Neural Networks
Year:
2010

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Abstract

An architecture for reasoning with pattern-based if-then rules is proposed. By processing patterns as real-valued vectors and classifying similar if-then rules into clusters in long-term memory, the proposed system can store pattern-based if-then rules of propositional logic, including conjunctions, disjunctions, and negations. Moreover, it achieves some important properties for intelligent systems such as incremental learning, generalization, avoidance of duplicate results, and robustness to noise. Results of experiments demonstrate that the proposed method is effective for intelligent systems for solving various tasks autonomously in a real environment.