Making FF Faster in ADL Domains

The fast-forward planning system (FF), which obtains heuristics via a relaxed planning graph to guide the enforced hill-climbing search strategy, has shown excellent performance in most STRIPS domains. When it comes to ADL domains, FF handles actions with conditional effects in a way similar to factored expansion. The result is that enforced hill-climbing guided by the relaxed Graphplan always fails in some ADL domains. We have discovered that the reason behind this issue is the relaxed Graphplan´s inability to handle relationships between actions´ components. We propose a novel approach called delayed partly reasoning on a naive conditional-effects planning graph (DP-CEPG). We do not ignore action´s delete effects and consider restricted induced component mutual exclusions between factored expanded actions. Preliminary results show that enforced hill-climbing while guided by DP-CEPG gains obvious improvements in most ADL problems in terms of both solution length and runtime.