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• W4386987127 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." @default.
• W4386987127 created "2023-09-24" @default.
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• W4386987127 date "2023-01-01" @default.
• W4386987127 modified "2023-10-18" @default.
• W4386987127 title "Comparing Planning Domain Models Using Answer Set Programming" @default.
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• W4386987127 doi "https://doi.org/10.1007/978-3-031-43619-2_16" @default.
• W4386987127 hasPublicationYear "2023" @default.
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