Towards Feasible Higher-Dimensional Potential Heuristics

Daniel Fišer, Marcel Steinmetz

Keywords: potential heuristics, optimal planning, symbolic search

Abstract:

Potential heuristics assign numerical values
(potentials) to state features, where each feature is a conjunction of
facts. It was previously shown that the informativeness of potential
heuristics can be significantly improved
by considering complex features,
but computing potentials over all pairs of facts
is already too costly in practice.
In this paper, we investigate whether using just a few high-dimensional
features instead of all conjunctions up to dimension $n$ can result in
improved heuristics while keeping the computational cost at bay. We focus on (a)
establishing a framework for studying this kind of potential heuristics, and
(b) whether it is reasonable to expect improvement with just a few
conjunctions. For (a), we propose two compilations that encode each
conjunction explicitly as a new fact so that we can compute
potentials over conjunctions in the original task as one-dimensional
potentials in the compilation.
Regarding (b), we provide evidence that informativeness of potential
heuristics can be significantly increased with a small set of conjunctions,
and these improvements have positive impact on the number of solved tasks.

Category: Long

Student: No

Submission Number: 123