Mundane reasoning by settling on a plausible model

摘要

Connectionist networks are well suited to everyday common sense reasoning. Their ability to simultaneously satisfy multiple soft constraints allows them to select from conflicting information in finding a plausible interpretation of a situation. However these networks are poor at reasoning using the standard semantics of classical logic, based on truth in all possible models. This article shows that using an alternate semantics, based on truth in a single most plausible model, there is an elegant mapping from theories expressed using the syntax of propositional logic onto connectionist networks. An extension of this mapping to allow for limited use of quantifiers suffices to build a network from knowledge bases expressed in a frame language similar to KL-ONE. Although finding optimal models of these theories is intractable, the networks admit a hill climbing search algorithm that can be tuned to give satisfactory answers in familiar situations. The article concludes with an example of retrieval involving incomplete and inconsistent information. Although this example works well, much remains before realistic domains are feasible.