Improving Plan Execution Flexibility using Block-Substitution
This work addresses plan flexibility for AI planning systems, offering an incremental improvement over traditional deordering and reordering methods.
The paper tackles the problem of improving plan execution flexibility in AI planning by substituting subplans with external actions, using block deordering and pruning techniques. The result shows significant flexibility improvements on benchmark problems from the International Planning Competitions, with maintained coverage and execution time.
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. We exploit block deordering, which eliminates orderings in a POP by encapsulating coherent actions in blocks, to construct action blocks as candidate subplans for substitutions. In addition, this paper introduces a pruning technique for eliminating 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.