Unified Planning: Modeling, manipulating and solving AI planning problems in Python

Automated planning is a branch of artificial intelligence aiming at finding a course of action that achieves specified goals, given a description of the initial state of a system and a model of possible actions. There are plenty of planning approaches working under different assumptions and with dif...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:SoftwareX Jg. 29; S. 102012
Hauptverfasser: Micheli, Andrea, Bit-Monnot, Arthur, Röger, Gabriele, Scala, Enrico, Valentini, Alessandro, Framba, Luca, Rovetta, Alberto, Trapasso, Alessandro, Bonassi, Luigi, Gerevini, Alfonso Emilio, Iocchi, Luca, Ingrand, Felix, Köckemann, Uwe, Patrizi, Fabio, Saetti, Alessandro, Serina, Ivan, Stock, Sebastian
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.02.2025
Elsevier
Schlagworte:
Artificial Intelligence
Automated planning and scheduling
Computer Science
Interoperability
Python library
Software Engineering
Python library
Interoperability
Automated planning and scheduling
Automated planning and scheduling Python library Interoperability
ISSN:2352-7110, 2352-7110
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Automated planning is a branch of artificial intelligence aiming at finding a course of action that achieves specified goals, given a description of the initial state of a system and a model of possible actions. There are plenty of planning approaches working under different assumptions and with different features (e.g. classical, temporal, and numeric planning). When automated planning is used in practice, however, the set of required features is often initially unclear. The Unified Planning (UP) library addresses this issue by providing a feature-rich Python API for modeling automated planning problems, which are solved seamlessly by planning engines that specify the set of features they support. Once a problem is modeled, UP can automatically find engines that can solve it, based on the features used in the model. This greatly reduces the commitment to specific planning approaches and bridges the gap between planning technology and its users.
ISSN:2352-7110
2352-7110
DOI:10.1016/j.softx.2024.102012