Exploiting Procedural Domain Control Knowledge in State-of-the-Art Planners

All Papers

Domain control knowledge (DCK) has proven effective in improving the efficiency of plan generation by reducing the search space for a plan. Procedural DCK is a compelling type of DCK that supports a natural specification of the skeleton of a plan. Unfortunately, most state-of-the-art planners do not have the machinery necessary to exploit procedural DCK. To resolve this deficiency, we propose to compile procedural DCK directly into PDDL2.1, thus enabling any PDDL2.1- compatible planner to exploit it. The contribution of this paper is threefold. First, we propose a PDDL-based semantics for an Algol-like, procedural language that can be used to specify DCK in planning. Second, we provide a polynomial algorithm that translates an ADL planning instance and a DCK program, into an equivalent, program-free PDDL2.1 instance whose plans are only those that adhere to the program. Third, we argue that the resulting planning instance is well-suited to being solved by domain-independent heuristic planners. To this end, we propose three approaches to computing domain-independent heuristics for our translated instances, sometimes leveraging properties of our translation to guide search. In our experiments on familiar PDDL planning benchmarks we show that the proposed compilation of procedural DCK can significantly speed up the performance of a heuristic search planner. Our translators are implemented and available on the web.