Communications of the ACM
Mundane reasoning by settling on a plausible model
Artificial Intelligence - On connectionist symbol processing
Learning and relearning in Boltzmann machines
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Model-preference default theories
Artificial Intelligence
Artificial Intelligence - Special volume on natural language processing
Circuits of the mind
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Robust reasoning: integrating rule-based and similarity-based reasoning
Artificial Intelligence
Tractable Reasoning in Artificial Intelligence
Tractable Reasoning in Artificial Intelligence
A computational model of tractable reasoning: taking inspiration from cognition
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
On the hardness of approximate reasoning
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Learning to reason the non monotonic case
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Solving large-scale constraint satisfaction and scheduling problems using a heuristic repair method
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
An optimally efficient limited inference system
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
A new method for solving hard satisfiability problems
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
On the adequateness of the connection method
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
Reasoning with characteristic models
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
STOC '99 Proceedings of the thirty-first annual ACM symposium on Theory of computing
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We present a connectionist architecture that supports almost instantaneous deductive and abductive reasoning. The deduction algorithm responds in few steps for single rule queries and in general, takes time that is linear with the number of rules in the query. The abduction algorithm produces an explanation in few steps and the best explanation in time linear with the size of the assumption set. The size of the network is polynomially related to the size of other representations of the domain, and may even be smaller. We base our connectionist model on Valiant's Neuroidal model (Val94) and thus make minimal assumptions about the computing elements, which are assumed to be classical threshold elements with states. Within this model we develop a reasoning framework that utilizes a model-based approach to reasoning (KKS93; KR94b). In particular, we suggest to interpret the connectionist architecture as encoding examples of the domain we reason about and show how to perform various reasoning tasks with this interpretation. We then show that the representations used can be acquired efficiently from interactions with the environment and discuss how this learning process influences the reasoning performance of the network.