Summary of Improving Execution Concurrency in Partial-order Plans Via Block-substitution, by Sabah Binte Noor and Fazlul Hasan Siddiqui
Improving Execution Concurrency in Partial-Order Plans via Block-Substitution
by Sabah Binte Noor, Fazlul Hasan Siddiqui
First submitted to arxiv on: 25 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Partial-Order Plan (POP) allows for flexible execution and reusability by enabling actions to be executed in different sequences or concurrently. This flexibility is achieved through the incorporation of action non-concurrency constraints, which specify which actions cannot be executed in parallel. The paper formalizes the conditions for transforming a POP into a parallel plan and introduces an algorithm to optimize resource utilization by substituting subplans with respect to the corresponding planning task. The proposed algorithm employs block deordering, which encapsulates coherent actions in blocks and then exploits these blocks as candidate subplans for substitutions. Experimental results on benchmark problems from International Planning Competitions (IPC) show significant improvement in plan concurrency, with 25% of plans showing improved concurrency and an overall increase of 2.1%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A Partial-Order Plan is a way to make AI planning more flexible by letting actions be done in different orders or at the same time. This makes it easier to reuse plans, modify them, or break them down into smaller parts. The paper looks at how to use these plans and makes an algorithm that can optimize resource usage by swapping out sub-plans. The algorithm works by grouping together similar actions and then using those groups as building blocks for new plans. Tests on real problems show that this approach can make plans more concurrent, which is good because it means computers can do tasks faster. |
