Abstract
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 of domain models. 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.
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Notes
- 1.
Encoding and benchmarks are available at: https://github.com/MarcoMochi/jelia-planning.
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Acknowledgements
L. Chrpa was funded by the Czech Science Foundation (project no. 23-05575S). M. Vallati was supported by the UKRI Future Leaders Fellowship [grant number MR/T041196/1]. C. Dodaro was supported by Italian Ministry of Research (MUR) under PNRR projects FAIR “Future AI Research”, CUP H23C22000860006, and Tech4You “Technologies for climate change adaptation and quality of life improvement”, CUP H23C22000370006;
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Chrpa, L., Dodaro, C., Maratea, M., Mochi, M., Vallati, M. (2023). Comparing Planning Domain Models Using Answer Set Programming. In: Gaggl, S., Martinez, M.V., Ortiz, M. (eds) Logics in Artificial Intelligence. JELIA 2023. Lecture Notes in Computer Science(), vol 14281. Springer, Cham. https://doi.org/10.1007/978-3-031-43619-2_16
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