Comparing Planning Domain Models Using Answer Set Programming

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Comparing Planning Domain Models Using Answer Set Programming

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Comparing Planning Domain Models Using Answer Set Programming

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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

Projekt

<a href="/cs/project/GA23-05575S" target="_blank" >GA23-05575S: Řízení dopravy v obydlených oblastech pomocí automatického plánování</a><br>

Návaznosti

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

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

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

Počet stran výsledku

16

Strana od-do

227-242

Název nakladatele

Springer

Místo vydání

Basel

Místo konání akce

Dresden

Datum konání akce

20. 9. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001157340700016