@inbook{fbb1b37d6a1041f7ae72ed385e0523f4,
title = "Temporal Plan Quality Improvement and Repair using Local Search",
abstract = "This paper presents an approach to repair or improve the quality of plans which make use of temporal and numeric constructs. While current state-of-the-art temporal planners are biased towards minimising makespan, the focus of this approach is to maximise plan quality. Local search is used to explore the neighbourhood of an input seed plan and find valid plans of a better quality with respect to the specified cost function. Experiments show that this algorithm is effective to improve plans generated by other planners, or to perform plan repair when the problem definition changes during the execution of a plan.",
keywords = "local search, optimisation, plan repair, scheduling, temporal planning",
author = "Josef Bajada and Maria Fox and Derek Long",
year = "2014",
doi = "10.3233/978-1-61499-421-3-41",
language = "English",
isbn = "9781614994206",
volume = "264",
series = "Frontiers in Artificial Intelligence and Applications",
publisher = "IOS Press",
pages = "41--50",
booktitle = "Frontiers in Artificial Intelligence and Applications",
note = "7th European Starting AI Researcher Symposium, STAIRS 2014 ; Conference date: 18-08-2014 Through 19-08-2014",
}