Pattern Databases for Goal-Probability Maximization in Probabilistic Planning

Abstract

Heuristic search algorithms for goal-probability maximization (MaxProb) have been known since a decade. Yet prior work on heuristic functions for MaxProb relies on determinization, not actually taking the probabilities into account. Here we begin to fix this, by introducing MaxProb pattern databases (PDB). We show that, for the special case of PDBs in contrast to more general abstractions, abstract transitions have a unique probability so that the abstract planning task is still an MDP. The resulting heuristic functions are admissible, i.e., they upper-bound the real goal probability. We identify conditions allowing to admissibly multiply heuristic values across several PDBs. Our experiments show that even non-probabilistic PDB heuristics often outperform previous MaxProb heuristics, and that our new probabilistic PDBs can in turn yield significant performance gains over non-probabilistic ones.

Debugging a Policy: Automatic Action-Policy Testing in AI Planning

“…A natural question is whether such oracles can be obtained by drawing on the wealth of known optimistic and pessimistic approximations of planning, in particular the extensive work on admissible heuristic functions (e.g., Haslum and Geffner 2000;Edelkamp 2001;Helmert and Domshlak 2009;Helmert et al 2014;Pommerening et al 2015;Davies et al 2015;Trevizan, Thiébaux, and Haslum 2017;Klösner et al 2021). Can optimistic and/or pessimistic bounds be employed to show that a given policy behavior is necessarily sub-optimal?…”

“…ognized as a dead-end, and to 1 otherwise), which has also been deeply addressed (e.g.,Hoffmann, Kissmann, and Torralba 2014;Pommerening and Seipp 2016;Steinmetz and Hoffmann 2017). Some optimistic bounding methods beyond classical planning exist (e.g.,Domshlak and Mirkis 2015;Trevizan, Thiébaux, and Haslum 2017;Klösner et al …”