Nonmonotonic reasoning by inhibition nets Original Research Article

In this paper we will show that certain networks called ‘inhibition nets’ may be regarded as cognitive agents drawing nonmonotonic inferences. It will be proven that the system CL (introduced by KLM in [Artificial Intelligence 44 (1990) 186–189]) of nonmonotonic logic is both sound and complete with respect to the inferences drawn by finite hierarchical inhibition nets. The latter class of inhibition nets is shown to correspond to the class of finite, normal, hierarchical logic programs concerning dynamics, and also to the class of binary, layered, input-driven artificial neural networks.