Novel Approaches for Generalized Planning. Lotinac, D. Ph.D. Thesis, Pompeu Fabra University, 2017. ![Novel Approaches for Generalized Planning [pdf]](https://bibbase.org/img/filetypes/pdf.svg "pdf")
Dissertation abstract bibtex Classical planning is the problem of finding a sequence of actions, from a given initial state to some goal state. While, in generalized planning, a plan is a solution to a set of planning problems, which belong to the same class. In this thesis we explore novel ways of computing generalized plans, inductively from a set of examples, and deductively from a model of actions.First we present an extension of planning programs, as a representation of a generalized plan, which is induced from a set of examples. The extension, allows for modeling of classification tasks.This work also introduces a novel domain-independent algorithm for generating hierarchical task networks directly from the action model and one representative instance of the planning problem. We also present the optimizations used by the translation and show that the algorithm is competitive with the state-of-the-art algorithms.
@PhdThesis{Lotinac2017PhDThesis,
author = {Damir Lotinac},
title = {Novel Approaches for Generalized Planning},
school = {Pompeu Fabra University},
year = {2017},
abstract = {Classical planning is the problem of finding a sequence of actions, from a given initial state to some goal state. While, in generalized planning, a plan is a solution to a set of planning problems, which belong to the same class. In this thesis we explore novel ways of computing generalized plans, inductively from a set of examples, and deductively from a model of actions.First we present an extension of planning programs, as a representation of a generalized plan, which is induced from a set of examples. The extension, allows for modeling of classification tasks.This work also introduces a novel domain-independent algorithm for generating hierarchical task networks directly from the action model and one representative instance of the planning problem. We also present the optimizations used by the translation and show that the algorithm is competitive with the state-of-the-art algorithms.},
url_Dissertation = {https://www.tdx.cat/bitstream/handle/10803/458525/tdl.pdf}
}
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