摘要: The method of conditioning permits probabilistic inference in multiply connectedbelief networks using an algorithm by Pearl. This method uses a select set of nodes, the loop cutset, to render the multiply connected network singly ... 展开 The method of conditioning permits probabilistic inference in multiply connectedbelief networks using an algorithm by Pearl. This method uses a select set of nodes, the loop cutset, to render the multiply connected network singly connected. We discuss the function of the nodes of the loop cutset and a condition that must be met by the nodes of the loop cutset. We show that the problem of finding a loop cutset that optimizes probabilistic inference using the method of conditioning is NP-hard. We present a heuristic algorithm for finding a small loop cutset in polynomial time, and we analyze the performance of this heuristic algorithm empirically. Keywords: Artificial intelligence, Bayes theorem, Expert systems, Probabilistic reasoning, Belief networks, Multiply connected, Cutset loops, Reprints, Algorithms. (CP) 收起