AltAltp: Online Parallelization of Plans with Heuristic State Search

TL;DR

AltAltp is a heuristic state search planner that efficiently generates high-quality parallel plans by greedy online parallelization, leveraging novel distance heuristics derived from planning graphs, outperforming disjunctive planners in speed.

Contribution

Introduces AltAltp, a variant of AltAlt, that uses greedy online parallelization with new heuristics to improve parallel plan generation in state space planning.

Findings

AltAltp produces high-quality parallel plans.

It operates at a fraction of the cost of disjunctive planners.

Empirical results demonstrate its effectiveness.

Abstract

Despite their near dominance, heuristic state search planners still lag behind disjunctive planners in the generation of parallel plans in classical planning. The reason is that directly searching for parallel solutions in state space planners would require the planners to branch on all possible subsets of parallel actions, thus increasing the branching factor exponentially. We present a variant of our heuristic state search planner AltAlt, called AltAltp which generates parallel plans by using greedy online parallelization of partial plans. The greedy approach is significantly informed by the use of novel distance heuristics that AltAltp derives from a graphplan-style planning graph for the problem. While this approach is not guaranteed to provide optimal parallel plans, empirical results show that AltAltp is capable of generating good quality parallel plans at a fraction of the cost incurred by the disjunctive planners.