Automating Inhabitant Interactions In Home And Workplace Environments Through Data-driven Generation Of Hierarchical Partially-observable Markov Decision Processes

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dc.contributor Youngblood , Gregory Michael en_US
dc.date.accessioned 2007 -08 -23T01 :55 :56Z
dc.date.accessioned 2011 -08 -24T21 :39 :36Z
dc.date.available 2007 -08 -23T01 :55 :56Z
dc.date.available 2011 -08 -24T21 :39 :36Z
dc.date.issued 2007 -08 -23T01 :55 :56Z
dc.date.submitted July 2005 en_US
dc.identifier.uri http : / /hdl .handle .net /10106 /31
dc.description.abstract Markov models provide a useful representation of system behavioral actions and state observations , but they do not scale well . Utilizing a hierarchy and abstraction through hierarchical hidden Markov models (HHMMs ) improves scalability , but these structures are usually constructed manually using knowledge engineering techniques . We introduce a new method of automatically constructing HHMMs using the output of a sequential data -mining algorithm , Episode Discovery , and apply it to solving automation problems in the intelligent environment domain . Repetitive behavioral actions in sensor rich environments such as smart homes can be observed and categorized into periodic and frequent episodes through data -mining techniques utilizing the minimum description length principle . Utilizing this approach , we provide an architecture and a set of algorithms for a pervasive computing system showing that inhabitant interactions in home and workplace environments can be accurately automated through sensor observation and intelligent control using a data -driven approach to automatically generate hierarchical inhabitant interaction models in the form of HPOMDPs and these models may be modified using temporal -difference reinforcement learning techniques to continually adapt to changes in the inhabitant's patterns until a new model should be generated . We present our life -long learning system and apply this work in our MavPad and MavLab environments where we have been successful at automating up to 40 % of the life of a real inhabitant and 76 % of a virtual inhabitant as well as dynamically adapting to concept changes over time . Findings from several case studies are provided to show the feasibility of this approach . en_US
dc.language.iso EN en_US
dc.publisher Computer Science & Engineering en_US
dc.title Automating Inhabitant Interactions In Home And Workplace Environments Through Data -driven Generation Of Hierarchical Partially -observable Markov Decision Processes en_US
dc.type Ph .D . en_US

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Automating Inhabitant Interactions In Home And Workplace Environments Through Data-driven Generation Of Hierarchical Partially-observable Markov Decision Processes. Available electronically from http : / /hdl .handle .net /10106 /31 .

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