Parallel reasoning in recursive causal networks

Abstract

Reasoning under uncertainty is one of the most important challenges in expert systems and some other branches of AI. Computational efficiency is a primary problem in implementing any practical system. In order to improve computational efficiency, several methods have been proposed to exploit the parallelism inherent in reasoning under uncertainty. However, some of these models can be used only in the case of singly connected networks, and one allows only one direction of reasoning. A parallel reasoning method based on the minimum cross entropy principle and the concept of recursive causal models is proposed to avoid the disadvantages of the methods.<>