Work

On the Gains from Using Action Observations in Domain Repair

Year: 2025

Type: conference-paper

Abstract: Designing a PDDL planning domain is an error-prone task, which can result in unsolvable planning tasks or unexpected plans. Existing domain repair methods either rely on a complete plan to identify unsatisfied preconditions or operate without any input plan by compiling the flawed planning task into a new planning task with self-repair actions. In contrast, learning approaches often benefit from a range of input observations to infer domain models. In this paper, we extend the self-repair compilation to also accept as input a variable number of action observations. Experimental results show improved domain repair quality and generally strong performance compared to previous domain repair and learning methods.

Source: Proceedings of the International Conference on Automated Planning and Scheduling

DOI: 10.1609/icaps.v35i1.36136

Authors: Alba Gragera, Raquel Fuentetaja, Ángel García Olaya, Fernando Fernández

Institution: Universidad Carlos III de Madrid

Language: English

Topic: AI-based Problem Solving and Planning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

OpenAlex ID: W4414229870

FWCI:

Cites:

Cited by:

Related to: