Planning with Critical Section Macros: Theory and Practice

Abstract

Macro-operators (macros) are a well-known technique for enhancing performance of planning engines by providing “short-cuts” in the state space. Existing macro learning systems usually generate macros by considering most frequent action sequences in training plans. Unfortunately, frequent action sequences might not capture meaningful activities as a whole, leading to a limited beneficial impact for the planning process.
 In this paper, inspired by resource locking in critical sections in parallel computing, we propose a technique that generates macros able to capture whole activities in which limited resources (e.g., a robotic hand, or a truck) are used. Specifically, such a Critical Section macro starts by locking the resource (e.g., grabbing an object), continues by using the resource (e.g., manipulating the object) and finishes by releasing the resource (e.g., dropping the object). Hence, such a macro bridges states in which the resource is locked and cannot be used. We also introduce versions of Critical Section macros dealing with multiple resources and phased locks. Usefulness of macros is evaluated using a range of state-of-the-art planners, and a large number of benchmarks from the deterministic and learning tracks of recent editions of the International Planning Competition.

Reformulation Techniques for Automated Planning: A Systematic Review

“…As mentioned in previous sections, macros are frequently combined with other reformulation techniques. For instance, they have been used with entanglements [Chrpa et al, 2014], with action elimination [Chrpa and Vallati, 2022], or with domain model configuration .…”

Reformulation techniques for automated planning: a systematic review

“…As mentioned in previous sections, macros are frequently combined with other reformulation techniques. For instance, they have been used with entanglements (Chrpa et al, 2014), with action elimination (Chrpa & Vallati, 2022), or with domain model configuration (Vallati et al, 2021). While domain model configuration can be straightforwardly combined with any other reformulation technique, the combination of macros and entanglements can lead to significant performance improvements, as entanglements are tackling the main drawback that is associated with the use of macros, that is the branching factor.…”

“…For instance, they have been used with entanglements (Chrpa et al. , 2014), with action elimination (Chrpa & Vallati, 2022), or with domain model configuration (Vallati et al. , 2021).…”