Comparing Planning Domain Models using Answer Set Programming

Lukáš Chrpa; Carmine Dodaro; Marco Maratea; Marco Mochi; Mauro Vallati

doi:10.1007/978-3-031-43619-2_16

Comparing Planning Domain Models using Answer Set Programming

Lukáš Chrpa, Carmine Dodaro, Marco Maratea, Marco Mochi, Mauro Vallati

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

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.

Original language	English
Title of host publication	Logics in Artificial Intelligence
Subtitle of host publication	18th European Conference, JELIA 2023, Dresden, Germany, September 20–22, 2023, Proceedings
Editors	Sarah Gaggl, Maria Vanina Martinez, Magdalena Ortiz
Place of Publication	Cham
Publisher	Springer, Cham
Pages	227-242
Number of pages	16
Edition	1st
ISBN (Electronic)	9783031436192
ISBN (Print)	9783031436185
DOIs	https://doi.org/10.1007/978-3-031-43619-2_16
Publication status	Published - 24 Sep 2023
Event	18th Edition of the European Conference on Logics in Artificial Intelligence - International Center for Computational Logic of TU Dresden, Dresden, Germany Duration: 20 Sep 2023 → 22 Sep 2023 Conference number: 18 https://jelia2023.inf.tu-dresden.de/

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	14281 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	18th Edition of the European Conference on Logics in Artificial Intelligence
Abbreviated title	JELIA 2023
Country/Territory	Germany
City	Dresden
Period	20/09/23 → 22/09/23
Internet address	https://jelia2023.inf.tu-dresden.de/

Access to Document

10.1007/978-3-031-43619-2_16Licence: Unspecified

JELIA_2023___Similarity_and_ASPAccepted author manuscript, 301 KBLicence: Unspecified

Cite this

Chrpa, L., Dodaro, C., Maratea, M., Mochi, M., & Vallati, M. (2023). Comparing Planning Domain Models using Answer Set Programming. In S. Gaggl, M. V. Martinez, & M. Ortiz (Eds.), Logics in Artificial Intelligence: 18th European Conference, JELIA 2023, Dresden, Germany, September 20–22, 2023, Proceedings (1st ed., pp. 227-242). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14281 LNAI). Springer, Cham. https://doi.org/10.1007/978-3-031-43619-2_16

Chrpa, Lukáš ; Dodaro, Carmine ; Maratea, Marco et al. / Comparing Planning Domain Models using Answer Set Programming. Logics in Artificial Intelligence: 18th European Conference, JELIA 2023, Dresden, Germany, September 20–22, 2023, Proceedings. editor / Sarah Gaggl ; Maria Vanina Martinez ; Magdalena Ortiz. 1st. ed. Cham : Springer, Cham, 2023. pp. 227-242 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Comparing Planning Domain Models using Answer Set Programming",

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.",

keywords = "Automated Planning, Answer Set Programming, Domain Model",

author = "Luk{\'a}{\v s} Chrpa and Carmine Dodaro and Marco Maratea and Marco Mochi and Mauro Vallati",

note = "Funding Information: Acknowledgements. L. Chrpa was funded by the Czech Science Foundation (project no. 23-05575S). M. Vallati was supported by the UKRI Future Leaders Fellowship [grant number MR/T041196/1]. C. Dodaro was supported by Italian Ministry of Research (MUR) under PNRR projects FAIR “Future AI Research”, CUP H23C22000860006, and Tech4You “Technologies for climate change adaptation and quality of life improvement”, CUP H23C22000370006; Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.; 18th Edition of the European Conference on Logics in Artificial Intelligence, JELIA 2023 ; Conference date: 20-09-2023 Through 22-09-2023",

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Chrpa, L, Dodaro, C, Maratea, M, Mochi, M & Vallati, M 2023, Comparing Planning Domain Models using Answer Set Programming. in S Gaggl, MV Martinez & M Ortiz (eds), Logics in Artificial Intelligence: 18th European Conference, JELIA 2023, Dresden, Germany, September 20–22, 2023, Proceedings. 1st edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14281 LNAI, Springer, Cham, Cham, pp. 227-242, 18th Edition of the European Conference on Logics in Artificial Intelligence, Dresden, Saxony, Germany, 20/09/23. https://doi.org/10.1007/978-3-031-43619-2_16

Comparing Planning Domain Models using Answer Set Programming. / Chrpa, Lukáš; Dodaro, Carmine; Maratea, Marco et al.

Logics in Artificial Intelligence: 18th European Conference, JELIA 2023, Dresden, Germany, September 20–22, 2023, Proceedings. ed. / Sarah Gaggl; Maria Vanina Martinez; Magdalena Ortiz. 1st. ed. Cham : Springer, Cham, 2023. p. 227-242 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14281 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Comparing Planning Domain Models using Answer Set Programming

AU - Chrpa, Lukáš

AU - Dodaro, Carmine

AU - Maratea, Marco

AU - Mochi, Marco

AU - Vallati, Mauro

N1 - Conference code: 18

PY - 2023/9/24

Y1 - 2023/9/24

N2 - 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.

AB - 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.

KW - Automated Planning

KW - Answer Set Programming

KW - Domain Model

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DO - 10.1007/978-3-031-43619-2_16

M3 - Conference contribution

SN - 9783031436185

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 227

EP - 242

BT - Logics in Artificial Intelligence

A2 - Gaggl, Sarah

A2 - Martinez, Maria Vanina

A2 - Ortiz, Magdalena

PB - Springer, Cham

CY - Cham

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Y2 - 20 September 2023 through 22 September 2023

ER -

Chrpa L, Dodaro C, Maratea M, Mochi M, Vallati M. Comparing Planning Domain Models using Answer Set Programming. In Gaggl S, Martinez MV, Ortiz M, editors, Logics in Artificial Intelligence: 18th European Conference, JELIA 2023, Dresden, Germany, September 20–22, 2023, Proceedings. 1st ed. Cham: Springer, Cham. 2023. p. 227-242. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). Epub 2023 Sep 23. doi: 10.1007/978-3-031-43619-2_16

Comparing Planning Domain Models using Answer Set Programming

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