A Survey on Model Repair in AI Planning

Pascal Bercher; Sarath Sreedharan; Mauro Vallati

doi:10.24963/ijcai.2025/1152

A Survey on Model Repair in AI Planning

Pascal Bercher, Sarath Sreedharan, Mauro Vallati

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

3 Citations (Scopus)

Abstract

Accurate planning models are a prerequisite for the appropriate functioning of AI planning applications. Creating these models is, however, a tedious and error-prone task - even for planning experts. This makes the provision of automated modeling support essential. In this work, we differentiate between approaches that learn models from scratch (called domain model acquisition) and those that repair flawed or incomplete ones. We survey approaches for the latter, including those that can be used for domain repair but have been developed for other applications, discuss possible optimization metrics (i.e., which repaired model to aim at), and conclude with lines of research we believe deserve more attention.

Original language	English
Title of host publication	Proceedings of the 34th International Joint Conference on Artificial Intelligence
Subtitle of host publication	IJCAI 2025
Editors	James Kwok
Publisher	IJCAI Organization
Pages	10371-10380
Number of pages	10
ISBN (Electronic)	9781956792065
DOIs	https://doi.org/10.24963/ijcai.2025/1152
Publication status	Published - 16 Aug 2025
Event	34th International Joint Conference on Artificial Intelligence - Montreal, Canada Duration: 16 Aug 2025 → 22 Aug 2025 Conference number: 34 https://2025.ijcai.org/

Conference

Conference	34th International Joint Conference on Artificial Intelligence
Abbreviated title	IJCAI 2025
Country/Territory	Canada
City	Montreal
Period	16/08/25 → 22/08/25
Internet address	https://2025.ijcai.org/

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Cite this

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title = "A Survey on Model Repair in AI Planning",

abstract = "Accurate planning models are a prerequisite for the appropriate functioning of AI planning applications. Creating these models is, however, a tedious and error-prone task - even for planning experts. This makes the provision of automated modeling support essential. In this work, we differentiate between approaches that learn models from scratch (called domain model acquisition) and those that repair flawed or incomplete ones. We survey approaches for the latter, including those that can be used for domain repair but have been developed for other applications, discuss possible optimization metrics (i.e., which repaired model to aim at), and conclude with lines of research we believe deserve more attention.",

keywords = "AI Planning, Domain model acquisition, Planning systems",

author = "Pascal Bercher and Sarath Sreedharan and Mauro Vallati",

note = "Publisher Copyright: {\textcopyright} 2025 International Joint Conferences on Artificial Intelligence. All rights reserved.; 34th International Joint Conference on Artificial Intelligence, IJCAI 2025 ; Conference date: 16-08-2025 Through 22-08-2025",

year = "2025",

month = aug,

day = "16",

doi = "10.24963/ijcai.2025/1152",

language = "English",

pages = "10371--10380",

editor = "James Kwok",

booktitle = "Proceedings of the 34th International Joint Conference on Artificial Intelligence",

publisher = "IJCAI Organization",

url = "https://2025.ijcai.org/",

}

Bercher, P, Sreedharan, S & Vallati, M 2025, A Survey on Model Repair in AI Planning. in J Kwok (ed.), Proceedings of the 34th International Joint Conference on Artificial Intelligence: IJCAI 2025. IJCAI Organization, pp. 10371-10380, 34th International Joint Conference on Artificial Intelligence, Montreal, Quebec, Canada, 16/08/25. https://doi.org/10.24963/ijcai.2025/1152

TY - GEN

T1 - A Survey on Model Repair in AI Planning

AU - Bercher, Pascal

AU - Sreedharan, Sarath

AU - Vallati, Mauro

N1 - Conference code: 34

PY - 2025/8/16

Y1 - 2025/8/16

N2 - Accurate planning models are a prerequisite for the appropriate functioning of AI planning applications. Creating these models is, however, a tedious and error-prone task - even for planning experts. This makes the provision of automated modeling support essential. In this work, we differentiate between approaches that learn models from scratch (called domain model acquisition) and those that repair flawed or incomplete ones. We survey approaches for the latter, including those that can be used for domain repair but have been developed for other applications, discuss possible optimization metrics (i.e., which repaired model to aim at), and conclude with lines of research we believe deserve more attention.

AB - Accurate planning models are a prerequisite for the appropriate functioning of AI planning applications. Creating these models is, however, a tedious and error-prone task - even for planning experts. This makes the provision of automated modeling support essential. In this work, we differentiate between approaches that learn models from scratch (called domain model acquisition) and those that repair flawed or incomplete ones. We survey approaches for the latter, including those that can be used for domain repair but have been developed for other applications, discuss possible optimization metrics (i.e., which repaired model to aim at), and conclude with lines of research we believe deserve more attention.

KW - AI Planning

KW - Domain model acquisition

KW - Planning systems

UR - https://www.ijcai.org/proceedings/2025/

UR - https://www.scopus.com/pages/publications/105021823049

U2 - 10.24963/ijcai.2025/1152

DO - 10.24963/ijcai.2025/1152

M3 - Conference contribution

SP - 10371

EP - 10380

BT - Proceedings of the 34th International Joint Conference on Artificial Intelligence

A2 - Kwok, James

PB - IJCAI Organization

T2 - 34th International Joint Conference on Artificial Intelligence

Y2 - 16 August 2025 through 22 August 2025

ER -

A Survey on Model Repair in AI Planning

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