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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Accesso online: | https://arxiv.org/abs/2406.03091 |
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| _version_ | 1866917370436517888 |
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| author | Noor, Sabah Binte Siddiqui, Fazlul Hasan |
| author_facet | Noor, Sabah Binte Siddiqui, Fazlul Hasan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_03091 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Improving Plan Execution Flexibility using Block-Substitution Noor, Sabah Binte Siddiqui, Fazlul Hasan Artificial Intelligence 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. |
| title | Improving Plan Execution Flexibility using Block-Substitution |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2406.03091 |