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
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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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