AYALSOPLAN: Bitstate Pruning for State-Based Planning on Massively Parallel Compute Clusters
نویسندگان
چکیده
Many planning systems operate by performing a heuristic forward search in the problem state space. In large problems that approach fails, exhausting a computer’s memory due to the burden of storing problem states. Moreover, it is an open question exactly how that approach should be parallelized to take advantage of modern multiple-processor computers and the proliferation of massively parallel compute clusters. This extended abstract proposes an answer to this second question, while also going some way to addressing the memory prob-
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