Inference and Decomposition in Planning Using Causal Consistent Chains

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 a0 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 performing inference over them from which a path-based planner is obtained. We define the conditions under which a path is consistent, provide an heuristic estimate of the cost of achieving the goal along a consistent path, and introduce a planning algorithm that uses paths as decomposition backbones...

• Type: Conference
• Year: 2009
• Where: AIPS
• Authors: Nir Lipovetzky, Hector Geffner
• Keywords: AIPS 2009, Artificial Intelligence, Causal Chains, Essential Inferential Component, Planners Solve Problems