Unified Planning: Modeling, manipulating and solving AI planning problems in Python
Year: 2024
Type: software-paper
Abstract: 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.
Source: SoftwareX
Institutions: Fondazione Bruno Kessler, Centre National de la Recherche Scientifique, Laboratoire d'Analyse et d'Architecture des Systèmes, Institut National des Sciences Appliquées de Toulouse, Laboratoire des Sciences et Techniques de l’Information de la Communication et de la Connaissance, Université de Toulouse, University of Basel, University of Brescia, Sapienza University of Rome, Örebro University, German Research Centre for Artificial Intelligence
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $970
OpenAlex ID: W4405587450