Enhancing Temporal Planning Domains by Sequential Macro-actions (Extended Version)

TL;DR

This paper introduces sequential macro-actions for temporal planning, improving planner performance and plan quality in multi-agent domains with shared resources by ensuring macro-actions are always executable.

Contribution

It proposes a general concept of sequential temporal macro-actions that maintain plan applicability, addressing challenges of concurrency and resource sharing in temporal planning.

Findings

Improved plan quality across multiple domains.

Enhanced planner performance with macro-actions.

Successful application to real-world benchmark domains.

Abstract

Temporal planning is an extension of classical planning involving concurrent execution of actions and alignment with temporal constraints. Durative actions along with invariants allow for modeling domains in which multiple agents operate in parallel on shared resources. Hence, it is often important to avoid resource conflicts, where temporal constraints establish the consistency of concurrent actions and events. Unfortunately, the performance of temporal planning engines tends to sharply deteriorate when the number of agents and objects in a domain gets large. A possible remedy is to use macro-actions that are well-studied in the context of classical planning. In temporal planning settings, however, introducing macro-actions is significantly more challenging when the concurrent execution of actions and shared use of resources, provided the compliance to temporal constraints, should not be suppressed entirely. Our work contributes a general concept of sequential temporal macro-actions that guarantees the applicability of obtained plans, i.e., the sequence of original actions encapsulated by a macro-action is always executable. We apply our approach to several temporal planners and domains, stemming from the International Planning Competition and RoboCup Logistics League. Our experiments yield improvements in terms of obtained satisficing plans as well as plan quality for the majority of tested planners and domains.