Attributed Transition-Based Domain Control Knowledge for Domain-Independent Planning

Abstract

Domain-independent planning decouples a planning task specification from planning engines. As the specification is usually describing only the physics of the environment, actions and a goal, the planning engines being generic solvers designed to solve any planning task tend to struggle with tasks that can be easily solved by domain-specific algorithms. Additional control knowledge can, to large extent, bridge such a performance gap. Instead of providing a specific planner supporting a given form of control knowledge, control knowledge can be directly encoded within the planning task specification and thus can be exploited by generic planners. In this paper, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Attributed Transition-Based Domain Control Knowledge (ATB-DCK)</i> that is represented by a finite state automaton with attributed states, referring to specific states of objects, connected by transitions imposing constraints on action applicability. ATB-DCK, roughly speaking, represents the “grammar” of solution plans that guides the search. We show that ATB-DCK can be compiled into a classical planning task and thus it complements domain-independent planning techniques. Using several domains from the International Planning Competitions as benchmarks, we demonstrate that this approach often considerably improves efficiency of existing state-of-the-art planning engines.