Abstract
A key technique for proving unsolvability in classical planning are dead-end detectors ?: 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 ?, where T can be
escaped, but only into dead-end states detected by
?. We show how to learn compact representations
of such T during search, extending the reach of ?.
Our experiments show that this can be quite beneficial. It improves coverage for many unsolvable
benchmark planning domains and dead-end detectors ?, in particular on resource-constrained domains where it outperforms the state of the art