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 Attributed Transition-Based Domain Control Knowledge (ATB-DCK) 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.