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- W3023846598 abstract "The difficulty of classical planning increases exponentially with search-tree depth. Heuristic search can make planning more efficient, but good heuristics often require domain-specific assumptions and may not generalize to new problems. Rather than treating the planning problem as fixed and carefully designing a heuristic to match it, we instead construct macro-actions that support efficient planning with the simple and general-purpose heuristic. Our approach searches for macro-actions that modify only a small number of state variables (we call this measure entanglement). We show experimentally that reducing entanglement exponentially decreases planning time with the goal-count heuristic. Our method discovers macro-actions with disentangled effects that dramatically improve planning efficiency for 15-puzzle and Rubik's cube, reliably solving each domain without prior knowledge, and solving Rubik's cube with orders of magnitude less data than competing approaches." @default.
- W3023846598 created "2020-05-13" @default.
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- W3023846598 date "2020-04-28" @default.
- W3023846598 modified "2023-09-27" @default.
- W3023846598 title "Finding Macro-Actions with Disentangled Effects for Efficient Planning with the Goal-Count Heuristic" @default.
- W3023846598 hasPublicationYear "2020" @default.
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