A Survey on Model Repair in AI Planning

Pascal Bercher; Sarath Sreedharan; Mauro Vallati

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

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, which 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
Publisher	IJCAI Organization
Publication status	Accepted/In press - 29 Apr 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/

Embargoed Document

IJCAI_survey_model_repair
Accepted author manuscript, 211 KB
Licence: Unspecified

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, which 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.",

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AU - Bercher, Pascal

AU - Sreedharan, Sarath

AU - Vallati, Mauro

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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, which 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, which 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

M3 - Conference contribution

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

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Y2 - 16 August 2025 through 22 August 2025

ER -