We propose an architecture for reasoning with pattern-based if-then rules that is effective for intelligent systems like robots solving varying tasks autonomously in a real environment. The proposed system can store pattern-based if-then rules of propositional logic, including conjunctions, disjunctions, negations, and implications. The naive pattern-based reasoning can store pattern-based if-then rules and make inferences using them. However, it remains insufficient for intelligent systems operating in a real environment. The proposed system uses an algorithm that is inspired by self-incremental neural networks such as SONIN and SOINN-AM in order to achieve incremental learning, generalization, avoidance of duplicate results, and robustness to noise, which are important properties for intelligent systems © 2008 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Sudo, A., Tsuboyama, M., Zhang, C., Sato, A., & Hasegawa, O. (2008). Pattern-based reasoning system using self-incremental neural network for propositional logic. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4984 LNCS, pp. 338–347). https://doi.org/10.1007/978-3-540-69158-7_36
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