Improving Execution Concurrency in Partial-Order Plans via Block-Substitution

📰 ArXiv cs.AI

Improving execution concurrency in partial-order plans via block-substitution to reduce overall execution time

advanced Published 1 Apr 2026
Action Steps
  1. Identify partial-order plans with potential for concurrent execution
  2. Apply block-substitution to enable parallel execution of actions
  3. Analyze and optimize the resulting execution plan to minimize dependencies and maximize concurrency
  4. Implement the optimized plan in a suitable execution framework
Who Needs to Know This

AI researchers and engineers working on planning and execution systems can benefit from this research to improve the efficiency of their systems, and software engineers can apply these concepts to optimize concurrent execution in various applications

Key Insight

💡 Block-substitution can be used to enable parallel execution of actions in partial-order plans, reducing overall execution time

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Key Takeaways

Improving execution concurrency in partial-order plans via block-substitution to reduce overall execution time

Full Article

Title: Improving Execution Concurrency in Partial-Order Plans via Block-Substitution

Abstract:
arXiv:2406.18615v2 Announce Type: replace Abstract: Partial-order plans in AI planning facilitate execution flexibility and several other tasks, such as plan reuse, modification, and decomposition, due to their less constrained nature. A \acrfull*{pop} specifies partial-order over actions, providing the flexibility of executing unordered actions in different sequences. This flexibility can be further extended by enabling parallel execution of actions in the POP to reduce its overall execution ti
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