Improving Plan Execution Flexibility using Block-Substitution

The paper by Noor and Siddiqui presents a novel algorithm called FIBS (Flexibility Improvement via Block-Substitution) to enhance the execution flexibility of Partial-order plans (POPs) in artificial intelligence planning, by substituting blocks of actions and pruning redundant actions, leading to more flexible and cost-effective plans.

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. 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.

Method

The authors used a methodology called Flexibility Improvement via Block-Substitution (FIBS), which is an iterative, anytime algorithm designed to enhance the flexibility of Partial-order plans (POPs) in artificial intelligence planning. The FIBS algorithm operates by identifying blocks of actions within a plan and iteratively substituting them to improve the plan's flexibility. This process involves several phases, including Explanation-based Order Generalization (EOG), block deordering (BD), and two stages of substitution (SD1 and SD2). Additionally, the authors introduced a strategy for pruning redundant actions to further optimize the plans. The effectiveness of FIBS was evaluated using the flexibility-coverage (fc) score, a new metric that considers both the flexibility of a plan and the coverage of the method in finding solutions. The experimental results showed that FIBS significantly improves plan flexibility and cost-effectiveness compared to other methods like MaxSAT reorderings.

Main Finding

The main finding of the paper "Improving Plan Execution Flexibility using Block-Substitution" by Noor and Siddiqui is that their proposed algorithm, FIBS (Flexibility Improvement via Block-Substitution), significantly enhances the execution flexibility of Partial-order plans (POPs) in artificial intelligence planning. FIBS achieves this by iteratively substituting blocks of actions within a plan, which allows for greater adaptability in dynamic environments. The algorithm also incorporates a strategy for pruning redundant actions, leading to a reduction in the overall cost of executing the plan. The experimental results demonstrate that FIBS outperforms existing methods like MaxSAT reorderings in terms of flexibility and cost-effectiveness across various planning domains. The paper also introduces a new metric, the flexibility-coverage (fc) score, which evaluates planning methods based on both flexibility and the ability to find solutions.

Conclusion

The conclusion of the paper is that the proposed Flexibility Improvement via Block-Substitution (FIBS) algorithm significantly enhances the flexibility of Partial-order plans (POPs) in artificial intelligence planning. FIBS achieves this by iteratively substituting blocks of actions within a plan, which allows for greater adaptability in dynamic environments. The algorithm also incorporates a strategy for pruning redundant actions, leading to a reduction in the overall cost of executing the plan. The experimental results demonstrate that FIBS outperforms existing methods like MaxSAT reorderings in terms of flexibility and cost-effectiveness across various planning domains. The paper suggests that while FIBS is effective in improving plan flexibility and cost, there are potential areas for further research, such as exploring alternative approaches to block formation and applying the concept of block-substitution to other planning problem settings.

Keywords

Improving Plan Execution Flexibility, Block-Substitution, Partial-order plans, POPs, FIBS, Explanation-based Order Generalization, EOG, Block Deordering, BD, Plan Deordering, Plan Reordering, Artificial Intelligence Planning, Planning Domain Definition Language, PDDL, STRIPS, MaxSAT, Plan Flexibility, Coverage, fc score, Deordering, Reordering, Iterative Algorithm, Local Enhancement, Computational Cost, Coverage Metric, Flexibility Metric, Planning Graph Analysis, Hierarchical Temporal Planning, HTN, Conditional Effects, Validation Structure, Causal Structures, Petri Net Unfolding, Theorem Proving, Problem Solving, Multi-Agent System Architecture, Combinatorial Optimization, Planning Formalisms, Action Elimination, Plan Neighborhood Graph Search, Plan Modification, Plan Reuse, Validation-Structure-Based Theory, Acyclic, Transitively Closed, Operators, Initial State, Goal State, Formulae, Propositional Planning, Finite-Domain Representations, Planning System, Unfolding, Causal Structures, Validation Structure, Multi-Agent Systems, Combinatorial Optimization, Planning Formalisms, Action Elimination, Plan Neighborhood Graph Search, Plan Modification, Plan Reuse, Validation-Structure-Based Theory, Acyclic, Transitively Closed, Operators, Initial State, Goal State