One Repair to Rule Them All: Repairing a Broken Planning Domain Using Multiple Instances. Gragera, A. & Muise, C. In Workshop on Reliable Data-Driven Planning and Scheduling (RDDPS). 2024. ![One Repair to Rule Them All: Repairing a Broken Planning Domain Using Multiple Instances [pdf]](https://bibbase.org/img/filetypes/pdf.svg "pdf")
Paper abstract bibtex 2 downloads AI planners usually require an accurate description of the planning task to obtain a solution that achieves the goals of the problem. However, generating such descriptions can be time-consuming and error-prone, often resulting in unsolvable planning tasks. Planners lack the ability to identify semantic errors or explain how to solve them. Previous works offer repaired initial states/domains to make the planning task solvable. Still, they are limited to fixing things for a single problem instance or rely on input plan traces. In this work, we address the reparation of a flawed domain only based on a set of planning instances viewed holistically. By obtaining individual repairs for each problem, we search in the space of models for a repaired one that covers the full problem set. Our experimental results show the effective application of this approach in repairing a lifted planning domain and establish metrics that quantify the effort required to obtain the ground truth repair through an anytime algorithm.
@incollection{gragera-rddps-2024,
title = {One Repair to Rule Them All: Repairing a Broken Planning Domain Using Multiple Instances},
author = {Alba Gragera and Christian Muise},
booktitle = {Workshop on Reliable Data-Driven Planning and Scheduling (RDDPS)},
year = {2024},
url_paper = {https://icaps24.icaps-conference.org/program/workshops/rddps-papers/Gragera-RDDPS24.pdf},
abstract = {AI planners usually require an accurate description of the planning task to obtain a solution that achieves the goals of the problem. However, generating such descriptions can be time-consuming and error-prone, often resulting in unsolvable planning tasks. Planners lack the ability to identify semantic errors or explain how to solve them. Previous works offer repaired initial states/domains to make the planning task solvable. Still, they are limited to fixing things for a single problem instance or rely on input plan traces. In this work, we address the reparation of a flawed domain only based on a set of planning instances viewed holistically. By obtaining individual repairs for each problem, we search in the space of models for a repaired one that covers the full problem set. Our experimental results show the effective application of this approach in repairing a lifted planning domain and establish metrics that quantify the effort required to obtain the ground truth repair through an anytime algorithm.}
}
Downloads: 2
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