Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
All I know: a study in autoepistemic logic
Artificial Intelligence
Nonmonotonic reasoning, preferential models and cumulative logics
Artificial Intelligence
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
How logic emerges from the dynamics of information
Logic and information flow
General patterns in nonmonotonic reasoning
Handbook of logic in artificial intelligence and logic programming (vol. 3)
Foundations of logic programming
Principles of knowledge representation
Nonmonotonic reasoning by inhibition nets
Artificial Intelligence
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This paper is a sequel to Leitgeb. We show that certain networks called 'inhibition nets' may be regarded as mechanisms drawing nonmonotonic inferences if only an interpretation of net states as states of belief is introduced. We prove that each of the cumulative logical systems studied by Kraus et al. are sound and complete with respect to certain classes of such interpreted inhibition nets. Thus, there is an adequate cognitive network semantics for the systems C, CL, P, CM, and M of (nonmonotonic) logic.