Comparing Planning Domain Models using Answer Set Programming

Lukáš Chrpa, Carmine Dodaro, Marco Maratea, Marco Mochi, Mauro Vallati

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Automated planning is a prominent area of Artificial Intelligence, and an important component for intelligent autonomous agents. A critical aspect of domain-independent planning is the domain model, that encodes a formal representation of domain knowledge needed to reason upon a given problem. Despite the crucial role of domain models in automated planning, there is lack of tools supporting knowledge engineering process by comparing different versions of the models, in particular, determining and highlighting differences the models have. In this paper, we build on the notion of strong equivalence of domain models and formalise a novel concept of similarity. To measure the similarity of two models, we introduce a directed graph representation of lifted domain models that allows to formulate the domain model similarity problem as a variant of the graph edit distance problem. We propose an Answer Set Programming approach to optimally solve the domain model similarity problem, that identifies the minimum number of modifications the models need to become strongly equivalent, and we demonstrate the capabilities of the approach on a range of benchmark models.
Original languageEnglish
Title of host publicationProceedings of the 18th European Conference on Logics in Artificial Intelligence
Subtitle of host publicationJELIA 2023
PublisherSpringer, Cham
Publication statusAccepted/In press - 10 Jul 2023
Event18th Edition of the European Conference on Logics in Artificial Intelligence - International Center for Computational Logic of TU Dresden, Dresden, Germany
Duration: 20 Sep 202322 Sep 2023
Conference number: 18


Conference18th Edition of the European Conference on Logics in Artificial Intelligence
Abbreviated titleJELIA 2023
Internet address

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