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

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING SoftwareX Pub Date : 2025-02-01 Epub Date: 2024-12-19 DOI:10.1016/j.softx.2024.102012

Andrea Micheli , Arthur Bit-Monnot , Gabriele Röger , Enrico Scala , Alessandro Valentini , Luca Framba , Alberto Rovetta , Alessandro Trapasso , Luigi Bonassi , Alfonso Emilio Gerevini , Luca Iocchi , Felix Ingrand , Uwe Köckemann , Fabio Patrizi , Alessandro Saetti , Ivan Serina , Sebastian Stock

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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.

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统一规划：在Python中建模、操作和解决AI规划问题

自动规划是人工智能的一个分支，旨在找到实现特定目标的行动过程，给出系统的初始状态描述和可能的行动模型。有许多规划方法在不同的假设和不同的特征下工作（例如，经典规划、时间规划和数字规划）。然而，当在实践中使用自动化计划时，所需的特性集通常在一开始是不清楚的。统一规划（UP）库通过提供一个功能丰富的Python API来解决这个问题，该API用于对自动化规划问题进行建模，这些问题可以通过指定它们支持的特性集的规划引擎无缝地解决。一旦对问题进行建模，UP就可以根据模型中使用的特征自动找到可以解决问题的引擎。这大大减少了对具体规划方法的承诺，并弥合了规划技术与其用户之间的差距。

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来源期刊

SoftwareX COMPUTER SCIENCE, SOFTWARE ENGINEERING-

CiteScore

5.50

自引率

2.90%

发文量

184

审稿时长

9 weeks

期刊介绍： SoftwareX aims to acknowledge the impact of software on today''s research practice, and on new scientific discoveries in almost all research domains. SoftwareX also aims to stress the importance of the software developers who are, in part, responsible for this impact. To this end, SoftwareX aims to support publication of research software in such a way that: The software is given a stamp of scientific relevance, and provided with a peer-reviewed recognition of scientific impact; The software developers are given the credits they deserve; The software is citable, allowing traditional metrics of scientific excellence to apply; The academic career paths of software developers are supported rather than hindered; The software is publicly available for inspection, validation, and re-use. Above all, SoftwareX aims to inform researchers about software applications, tools and libraries with a (proven) potential to impact the process of scientific discovery in various domains. The journal is multidisciplinary and accepts submissions from within and across subject domains such as those represented within the broad thematic areas below: Mathematical and Physical Sciences; Environmental Sciences; Medical and Biological Sciences; Humanities, Arts and Social Sciences. Originating from these broad thematic areas, the journal also welcomes submissions of software that works in cross cutting thematic areas, such as citizen science, cybersecurity, digital economy, energy, global resource stewardship, health and wellbeing, etcetera. SoftwareX specifically aims to accept submissions representing domain-independent software that may impact more than one research domain.