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Improving AI Planning by Using Extensible Components

abstract: Despite incremental improvements over decades, academic planning solutions see relatively little use in many industrial domains despite the relevance of planning paradigms to those problems. This work observes four shortfalls of existing academic solutions which contribute to this lack of adoption.

To address these shortfalls this work defines model-independent semantics for planning and introduces an extensible planning library. This library is shown to produce feasible results on an existing benchmark domain, overcome the usual modeling limitations of traditional planners, and accommodate domain-dependent knowledge about the problem structure within the planning process. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2016

Identiferoai:union.ndltd.org:asu.edu/item:38756
Date January 2016
ContributorsJonas, Michael (Author), Gaffar, Ashraf (Advisor), Fainekos, Georgios (Committee member), Doupe, Adam (Committee member), Herley, Cormac (Committee member), Arizona State University (Publisher)
Source SetsArizona State University
LanguageEnglish
Detected LanguageEnglish
TypeDoctoral Dissertation
Format158 pages
Rightshttp://rightsstatements.org/vocab/InC/1.0/, All Rights Reserved

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