Effective plan retrieval in case-based planning for metric-temporal problems. Bonisoli, A., Gerevini, A. E., Saetti, A., & Serina, I. Journal of Experimental & Theoretical Artificial Intelligence, 27(5):603–647, September, 2015. ![Effective plan retrieval in case-based planning for metric-temporal problems [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Case-based planning (CBP) is an approach to planning where previous planning experience stored in a case base provides guidance to solving new problems. Such a guidance can be extremely useful when the new problem is very hard to solve, or the stored previous experience is highly valuable (because, e.g. it was provided and/or validated by human experts) and the system should try to reuse it as much as possible. In this work, we address CBP in PDDL domains with real-valued fluents, action durations and timed-initial literals, which are essential to model real-world planning problems involving continuous resources and temporal constraints. We propose some new heuristic techniques for retrieving a plan from a library of existing plans that is promising for solving a new planning problem encountered by the CBP system, i.e. that can be efficiently adapted to solve the new problem. The effectiveness of these techniques, which derive much of their power from the proposed use of the numerical/temporal information in the planning problem specification and in the library plans, is evaluated through an experimental analysis.
@article{bonisoli_effective_2015,
title = {Effective plan retrieval in case-based planning for metric-temporal problems},
volume = {27},
issn = {0952-813X},
url = {https://doi.org/10.1080/0952813X.2014.993506},
doi = {10.1080/0952813X.2014.993506},
abstract = {Case-based planning (CBP) is an approach to planning where previous planning experience stored in a case base provides guidance to solving new problems. Such a guidance can be extremely useful when the new problem is very hard to solve, or the stored previous experience is highly valuable (because, e.g. it was provided and/or validated by human experts) and the system should try to reuse it as much as possible. In this work, we address CBP in PDDL domains with real-valued fluents, action durations and timed-initial literals, which are essential to model real-world planning problems involving continuous resources and temporal constraints. We propose some new heuristic techniques for retrieving a plan from a library of existing plans that is promising for solving a new planning problem encountered by the CBP system, i.e. that can be efficiently adapted to solve the new problem. The effectiveness of these techniques, which derive much of their power from the proposed use of the numerical/temporal information in the planning problem specification and in the library plans, is evaluated through an experimental analysis.},
number = {5},
urldate = {2019-11-18},
journal = {Journal of Experimental \& Theoretical Artificial Intelligence},
author = {Bonisoli, Andrea and Gerevini, Alfonso Emilio and Saetti, Alessandro and Serina, Ivan},
month = sep,
year = {2015},
keywords = {case-based planning, metric-temporal planning, plan retrieval},
pages = {603--647}
}
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