Similarity of planning domain models via answer set programming

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 about a given problem. Despite the crucial role of domain models in automated planning, there is a lack of tools supporting the knowledge engineering process by comparing different versions of the models, in particular, determining and highlighting differences the models have. In this paper, we formalise the notions of structural equivalence and strong equivalence of domain models, and, on top of these, we introduce 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 the formulation of 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.