Improving Plan Execution Flexibility using Block-Substitution

TL;DR

This paper introduces a novel method for enhancing plan execution flexibility in AI planning by substituting subplans with external actions within a block-decomposed plan structure, showing significant improvements on benchmark problems.

Contribution

It proposes a new approach that improves plan flexibility through subplan substitution in block-decomposed plans, extending traditional deordering and reordering techniques.

Findings

Significant increase in plan flexibility on IPC benchmark problems

Maintains good coverage and execution time

Effective when combined with MaxSAT-based reorderings

Abstract

Partial-order plans in AI planning facilitate execution flexibility due to their less-constrained nature. Maximizing plan flexibility has been studied through the notions of plan deordering, and plan reordering. Plan deordering removes unnecessary action orderings within a plan, while plan reordering modifies them arbitrarily to minimize action orderings. This study, in contrast with traditional plan deordering and reordering strategies, improves a plan's flexibility by substituting its subplans with actions outside the plan for a planning problem. Our methodology builds on block deordering, which eliminates orderings in a POP by encapsulating coherent actions in blocks, yielding a hierarchically structured plan termed a Block Decomposed Partial-Order (BDPO) plan. We consider the action blocks in a BDPO plan as candidate subplans for substitutions, and ensure that each successful substitution produces a plan with strictly greater flexibility. In addition, this paper employs plan reduction strategies to eliminate redundant actions within a BDPO plan. We also evaluate our approach when combined with MaxSAT-based reorderings. Our experimental result demonstrates a significant improvement in plan execution flexibility on the benchmark problems from International Planning Competitions (IPC), maintaining good coverage and execution time.