Effect-Abstraction Based Relaxation for Linear Numeric Planning

This paper studies an effect-abstraction based relaxation for reasoning about linear numeric planning problems. The effect-abstraction decomposes non-constant linear numeric effects into actions with conditional effects over additive constant numeric effects. With little effort, on this compiled version, it is possible to use known subgoaling based relaxations and relative heuristics. The combination of these two steps leads to a novel relaxation based heuristic. Theoretically, the relaxation is proved tighter than previous interval based relaxation and leading to safe-pruning heuristics. Empirically, a heuristic developed on this relaxation leads to substantial improvements for a class of problems that are currently out of the reach of state-of-the-art numeric planners.