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Comparing Planning Domain Models Using Answer Set Programming

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F23%3A00369568" target="_blank" >RIV/68407700:21730/23:00369568 - isvavai.cz</a>

  • Result on the web

    <a href="http://dx.doi.org/10.1007/978-3-031-43619-2_16" target="_blank" >http://dx.doi.org/10.1007/978-3-031-43619-2_16</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1007/978-3-031-43619-2_16" target="_blank" >10.1007/978-3-031-43619-2_16</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Comparing Planning Domain Models Using Answer Set Programming

  • Original language description

    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.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

    <a href="/en/project/GA23-05575S" target="_blank" >GA23-05575S: Urban Traffic Control by Means of Automated Planning</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2023

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

  • ISBN

    978-3-031-43618-5

  • ISSN

    2945-9133

  • e-ISSN

    1611-3349

  • Number of pages

    16

  • Pages from-to

    227-242

  • Publisher name

    Springer

  • Place of publication

    Basel

  • Event location

    Dresden

  • Event date

    Sep 20, 2023

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article

    001157340700016