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.
|Journal||Journal of Artificial Intelligence Research|
|Publication status||Accepted/In press - 30 Nov 2021|