Search and learn: On dead-end detectors, the traps they set, and trap learning

Abstract

A key technique for proving unsolvability in classical planning are dead-end detectors Δ: effectively testable criteria sufficient for unsolvability, pruning (some) unsolvable states during search. Related to this, a recent proposal is the identification of traps prior to search, compact representations of non-goal state sets T that cannot be escaped. Here, we create new synergy across these ideas. We define a generalized concept of traps, relative to a given dead-end detector Δ, where T can be escaped, but only into dead-end states detected by Δ. We show how to learn compact representations of such T during search, extending the reach of Δ. Our experiments show that this can be quite beneficial. It improves coverage for many unsolvable benchmark planning domains and dead-end detectors Δ, in particular on resource-constrained domains where it outperforms the state of the art.