Lukáš Chrpa, Mauro Vallati

Critical Section Macros - New Results (Extended Abstract)

Introduction

Critical section macros - new results (extended abstract). Explore new empirical results for Critical Section Macro-operators (CSMs) in parallel computing. Evaluate CSM performance with top planners on IPC-2023 learning track domains.

Abstract

This extended abstract presents new empirical results of recently introduced Critical Section Macro-operators (CSMs) whose design is inspired by using lockable resources in critical sections in parallel computing. In particular, we provide results on the IPC-2023 learning track domains and four planners, including the winner of the agile track of the IPC-2023 and a lifted planner.

Review

This extended abstract presents timely new empirical results concerning Critical Section Macro-operators (CSMs), a concept inspired by the use of lockable resources in parallel computing critical sections. The interdisciplinary inspiration is particularly intriguing, suggesting a novel approach to tackling planning problems that might involve resource contention or sequential dependencies. As an extended abstract, it concisely introduces a practical contribution centered on empirical validation of these recently introduced operators. The methodology outlined suggests a robust evaluation framework. The authors indicate testing CSMs on the IPC-2023 learning track domains, which are recognized benchmarks in the planning community and represent a challenging set of problems. Furthermore, the selection of four diverse planners for comparison, including the winner of the IPC-2023 agile track and a lifted planner, significantly enhances the credibility of the findings. This broad comparison against state-of-the-art and different paradigms of planners is crucial for understanding the general applicability and specific strengths or weaknesses of CSMs. Overall, this abstract promises a valuable empirical contribution to the planning literature. While specific details of the "new results" are, understandably, not elaborated in this format, a full paper should provide a comprehensive analysis of *what* these results reveal – whether they demonstrate performance improvements, offer specific insights into certain problem types, or highlight limitations. A clear exposition of how the parallel computing inspiration directly translates into the design and benefits of CSMs would also be highly beneficial. If successful, this work could contribute to more effective heuristic design or domain modeling, particularly for complex planning scenarios.

Full Text

Comments

You need to be logged in to post a comment.