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Pattern-Based Reasoning System Using Self-incremental Neural Network for Propositional Logic

  • Akihito Sudo
  • Manabu Tsuboyama
  • Chenli Zhang
  • Akihiro Sato
  • Osamu Hasegawa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4984)

Abstract

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

Keywords

Reasoning neural network intelligent agent intelligent robot 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Akihito Sudo
    • 1
  • Manabu Tsuboyama
    • 1
  • Chenli Zhang
    • 1
  • Akihiro Sato
    • 1
  • Osamu Hasegawa
    • 1
  1. 1.Dept. of Computer Intelligence and Systems Science, Tokyo Institute of Technology Imaging Science and Engineering Lab.Tokyo Institute of TechnologyMidori-kuJapan

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