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

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 Partial-Order Plan (POP) allows two actions with no ordering between them, thus providing the flexibility of executing actions in different sequences. This flexibility can be further extended by enabling parallel execution of actions in a POP to reduce its overall execution time. While extensive studies exist on improving the flexibility of a POP by optimizing its action orderings through plan deordering and reordering, there has been limited focus on the flexibility of executing actions concurrently in a plan. Execution concurrency in a POP can be achieved by incorporating action non-concurrency constraints, specifying which actions can not be executed in parallel. This work formalizes the conditions for non-concurrency constraints to transform a POP into a parallel plan. We also introduce an algorithm to enhance the plan's concurrency by optimizing resource utilization through substitutions of its subplans with respect to the corresponding planning task. Our algorithm employs block deordering that eliminates orderings in a POP by encapsulating coherent actions in blocks, and then exploits blocks as candidate subplans for substitutions. Experiments over the benchmark problems from International Planning Competitions (IPC) exhibit significant improvement in plan concurrency, specifically, with improvement in 25% of the plans, and an overall increase of 2.1% in concurrency.