Full metadata
Title
Improving AI planning by using extensible components
Description
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.
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.
Date Created
2016
Contributors
- Jonas, Michael (Author)
- Gaffar, Ashraf (Thesis advisor)
- Fainekos, Georgios (Committee member)
- Doupe, Adam (Committee member)
- Herley, Cormac (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
vi, 151 pages : illustrations (some color)
Language
eng
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.I.38756
Statement of Responsibility
by Michael Jonas
Description Source
Viewed on August 9, 2016
Level of coding
full
Note
thesis
Partial requirement for: Ph.D., Arizona State University, 2016
bibliography
Includes bibliographical references (pages 131-135)
Field of study: Computer science
System Created
- 2016-06-01 08:59:25
System Modified
- 2021-08-30 01:22:43
- 3 years ago
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