A connectionist model for commonsense reasoning incorporating rules and similarities |
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| Institution: | 1. DeepMind, London, UK;2. Department of Computing, Imperial College London, London, UK;1. Hebei Key Laboratory of Data Science and Application, North China University of Science and Technology, Tangshan, 063000, China;2. Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes, North China University of Science and Technology, Tangshan, 063000, China;3. The Key Laboratory of Engineering Computing in Tangshan City, North China University of Science and Technology, Tangshan, 063000, China;1. Department of Parasitology, School of Medicine, China Medical University, Taichung, Taiwan;2. Department of Parasitology, College of Medicine, Chang Gung University, Taoyuan, Taiwan;3. Graduate Institute of Biomedical Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan;4. Molecular Infectious Disease Research Center, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan |
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| Abstract: | For the purpose of modelling commonsense reasoning, we investigate connectionist models of rule-based reasoning, and show that while such models can usually carry out reasoning in exactly the same way as symbolic systems, they have more to offer in terms of commonsense reasoning. A connectionist architecture, CONSYDERR, is proposed for capturing certain commonsense reasoning competence, which partially remedies the brittleness problem in traditional rule-based systems. The architecture employs a two-level, dual representational scheme, which utilizes both localist and distributed representations and explores the synergy resulting from the interaction between the two. CONSYDERR is therefore capable of accounting for many difficult patterns in commonsense reasoning with this simple combination of the two levels. This work shows that connectionist models of reasoning are not just “implementations” of their symbolic counterparts, but better computational models of commonsense reasoning. |
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