Active Sensing for Partially Observable Markov Decision Processes
| dc.contributor.author | Koltunova, Veronika | |
| dc.date.accessioned | 2013-01-21T19:46:56Z | |
| dc.date.available | 2013-01-21T19:46:56Z | |
| dc.date.issued | 2013-01-21T19:46:56Z | |
| dc.date.submitted | 2013-01-10 | |
| dc.description.abstract | Context information on a smart phone can be used to tailor applications for specific situations (e.g. provide tailored routing advice based on location, gas prices and traffic). However, typical context-aware smart phone applications use very limited context information such as user identity, location and time. In the future, smart phones will need to decide from a wide range of sensors to gather information from in order to best accommodate user needs and preferences in a given context. In this thesis, we present a model for active sensor selection within decision-making processes, in which observational features are selected based on longer-term impact on the decisions made by the smart phone. This thesis formulates the problem as a partially observable Markov decision process (POMDP), and proposes a non-myopic solution to the problem using a state of the art approximate planning algorithm Symbolic Perseus. We have tested our method on a 3 small example domains, comparing different policy types, discount factors and cost settings. The experimental results proved that the proposed approach delivers a better policy in the situation of costly sensors, while at the same time provides the advantage of faster policy computation with less memory usage. | en |
| dc.identifier.uri | http://hdl.handle.net/10012/7222 | |
| dc.language.iso | en | en |
| dc.pending | false | en |
| dc.publisher | University of Waterloo | en |
| dc.subject | Active Sensing | en |
| dc.subject | Smart Sensor Selection | en |
| dc.subject | POMDP | en |
| dc.subject | Markov Decision Process | en |
| dc.subject | Probability | en |
| dc.subject | Utility | en |
| dc.subject | Sensor | en |
| dc.subject | Networks | en |
| dc.subject.program | Computer Science | en |
| dc.title | Active Sensing for Partially Observable Markov Decision Processes | en |
| dc.type | Master Thesis | en |
| uws-etd.degree | Master of Mathematics | en |
| uws-etd.degree.department | School of Computer Science | en |
| uws.peerReviewStatus | Unreviewed | en |
| uws.scholarLevel | Graduate | en |
| uws.typeOfResource | Text | en |