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Protocols for Stochastic Shortest Path Problems with Dynamic Learning by

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Author(s)
Vural Aksakalli
Contributor(s)
The Pennsylvania State University CiteSeerX Archives

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URI
http://hdl.handle.net/20.500.12424/837798
Online Access
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.110.8955
http://www.ams.jhu.edu/~aksakalli/aa_dist.pdf
Abstract
The research problem considered in this dissertation, in its most broad setting, is a stochastic shortest path problem in the presence of a dynamic learning capability (SDL). Specifically, a spatial arrangement of possible-obstacles needs to be traversed as swiftly as possible, and the status of the obstacles may be disambiguated (at a cost) en route. No efficiently computable optimal protocol is known and many similar problems have been proven intractable. Chapter 1 defines SDL in continuous and discrete settings, and introduces the Ran-dom Disambiguation Paths Problem (RDP), a continuous variant of SDL wherein a decision maker (DM) needs to swiftly navigate from one given location to another through an arrangement of disc-shaped possible-obstacles in the plane. At the outset, the DM is given the respective probabilities that the discs are truly obstacles and, en route, when situated on a disc’s boundary, the DM has the option to disambiguate the disc, i.e., learn at a cost if the disc is truly an obstacle. The central question is to find a protocol that decides what and where to disambiguate en route so as to
Date
2008-08-14
Type
text
Identifier
oai:CiteSeerX.psu:10.1.1.110.8955
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.110.8955
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Metadata may be used without restrictions as long as the oai identifier remains attached to it.
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