A Planning Heuristic Based on Causal Graph Analysis

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In recent years, heuristic search methods for classical planning have achieved remarkable results. Their most successful representative, the FF algorithm, performs well over a wide spectrum of planning domains and still sets the state of the art for STRIPS planning. However, there are some planning domains in which algorithms like FF and HSP perform poorly because their relaxation method of ignoring the delete lists of STRIPS operators loses too much vital information. Planning domains which have many dead ends in the search space are especially problematic in this regard. In some domains, dead ends are readily found by the human observer yet remain undetected by all propositional planning systems we are aware of. We believe that this is partly because the STRIPS representation obscures the important causal structure of the domain, which is evident to humans. In this paper, we propose translating STRIPS problems to a planning formalism with multi-valued state variables in order to expose this underlying causal structure. Moreover, we show how this structure can be exploited by an algorithm for detecting dead ends in the search space and by a planning heuristic based on hierarchical problem decomposition. Our experiments show excellent overall performance on the benchmarks from the international planning competitions.