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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.
Created Date 2016
Contributor Jonas, Michael (Author) / Gaffar, Ashraf (Advisor) / Fainekos, Georgios (Committee member) / Doupe, Adam (Committee member) / Herley, Cormac (Committee member) / Arizona State University (Publisher)
Subject Computer science / Computer engineering / Robotics / Artificial Intelligence / Cyber-Physical Systems / Knowledge Representation / Numerical Modeling / Planning / Semantic Mapping
Type Doctoral Dissertation
Extent 158 pages
Language English
Copyright
Reuse Permissions All Rights Reserved
Note Doctoral Dissertation Computer Science 2016
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


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Description Dissertation/Thesis