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JELIA 2023

Enhancing Temporal Planning by Sequential Macro-Actions

Conference Paper Planning Artificial Intelligence · Knowledge Representation · Logic in Computer Science

Abstract

Abstract Temporal planning is an extension of classical planning involving concurrent execution of actions and alignment with temporal constraints. 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.

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Keywords

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Context

Venue
European Conference on Logics in Artificial Intelligence
Archive span
2000-2023
Indexed papers
542
Paper id
1074056033525067357
v2026.09.13