Symbolic Search for Cost-Optimal Planning with Expressive Model Extensions

Abstract

In classical planning, the task is to derive a sequence of deterministic actions that changes the current fully-observable world state into one that satisfies a set of goal criteria. Algorithms for classical planning are domain-independent, i.e., they are not limited to a particular application and instead can be used to solve different types of reasoning problems. The main language for modeling such problems is the Planning Domain Definition Language (PDDL). Even though it provides many language features for expressing a wide range of planning tasks, most of today’s classical planners, especially optimal ones, support only a small subset of its features. The most widely supported fragment is lifted STRIPS plus types and action costs. While this fragment suffices to model some interesting planning tasks, using it to model more realistic problems often incurs a much higher modeling effort. Even if modeling is possible at all, solving the resulting tasks is often infeasible in practice, as the required encoding size increases exponentially. To address these issues, we show how to support more expressive modeling languages natively in optimal classical planning algorithms. Specifically, we focus on symbolic search, a state-of-the-art search algorithm that operates on sets of world states. We show how to extend symbolic search to support classical planning with conditional effects, axioms, and state-dependent action costs. All of these modeling features are expressive in the sense that compiling them away incurs a significant blow-up, so is it often necessary to support them natively. Except for blind (non-symbolic) search, our new symbolic search is the first optimal classical planning algorithm that supports these three modeling extensions in combination, and it even compares favorably to other state-of-the-art approaches that only support a subset of the extensions.