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Recent work on LatPlan has shown that it is possible to learn models fordomain-independent classical planners from unlabeled image data. Although PDDLmodels acquired by LatPlan can be solved using standard PDDL planners, theresulting latent-space plan may be invalid with respect to the underlying,ground-truth domain (e.g., the latent-space plan may includehallucinatory/invalid states). We propose Plausibility-Based Heuristics, whichare domain-independent plausibility metrics which can be computed for eachstate evaluated during search and uses as a heuristic function for best-firstsearch. We show that PBH significantly increases the number of valid foundplans on image-based tile puzzle and Towers of Hanoi domains.

Plausibility-Based Heuristics for Latent Space Classical Planning