Inference and decomposition in planning using causal consistent chains

Abstract

Current state-of-the-art planners solve problems, easy and hard alike, by search, expanding hundreds or thousands of nodes. Yet, given the ability of people to solve easy problems and to explain their solutions, it seems that an essential inferential component may be missing. The reasons expressed by people for selecting actions appear to be related to causal chains: sequences of causal links ai → pi+1, i = 0,..., n - 1, such that a 0 is applicable in the current state, pi is a precondition of action ai, and pn is a goal. Some of these causal chains or paths appear to be good, some bad, others appear to be impossible. In this work, we focus on such paths and develop three techniques for perf..