Modeling and Understanding Human Routine Behavior
Identifieur interne : 000082 ( Main/Exploration ); précédent : 000081; suivant : 000083Modeling and Understanding Human Routine Behavior
Auteurs : Nikola Banovic [États-Unis] ; Tofi Buzali [États-Unis] ; Fanny Chevalier [France] ; Jennifer Mankoff [États-Unis] ; Anind Dey [États-Unis]Source :
English descriptors
Abstract
Human routines are blueprints of behavior, which allow people to accomplish purposeful repetitive tasks at many levels, ranging from the structure of their day to how they drive through an intersection. People express their routines through actions that they perform in the particular situations that triggered those actions. An ability to model routines and understand the situations in which they are likely to occur could allow technology to help people improve their bad habits, inexpert behavior, and other suboptimal routines. However, existing routine models do not capture the causal relationships between situations and actions that describe routines. Our main contribution is the insight that byproducts of an existing activity prediction algorithm can be used to model those causal relationships in routines. We apply this algorithm on two example datasets, and show that the modeled routines are meaningful—that they are predictive of people's actions and that the modeled causal relationships provide insights about the routines that match findings from previous research. Our approach offers a generalizable solution to model and reason about routines.
Url:
DOI: 10.1145/2858036.2858557
Affiliations:
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Le document en format XML
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Carnegie Mellon University</orgName> <desc> <address> <addrLine>Computer Science Department Carnegie Mellon University Pittsburgh, PA</addrLine> <country key="US"></country> </address> <ref type="url">http://www.cs.cmu.edu/</ref> </desc> <listRelation> <relation active="#struct-378064" type="direct"></relation> </listRelation> <tutelles><tutelle active="#struct-378064" type="direct"><org type="institution" xml:id="struct-378064" status="INCOMING"> <orgName>University of Pittsburgh</orgName> <desc> <address> <country key="FR"></country> </address> </desc> </org></tutelle></tutelles></hal:affiliation><country>États-Unis</country><placeName><settlement type="city">Pittsburgh</settlement><region type="state">Pennsylvanie</region></placeName><orgName type="university">Université de Pittsburgh</orgName></affiliation></author><author><name sortKey="Dey, Anind" sort="Dey, Anind" uniqKey="Dey A" first="Anind" last="Dey">Anind Dey</name><affiliation wicri:level="1"><hal:affiliation type="laboratory" xml:id="struct-87723" status="VALID"> <orgName>Computer Science Department - Carnegie Mellon University</orgName> <desc> <address> <addrLine>Computer Science Department Carnegie Mellon University Pittsburgh, PA</addrLine> <country key="US"></country> </address> <ref type="url">http://www.cs.cmu.edu/</ref> </desc> <listRelation> <relation active="#struct-378064" type="direct"></relation> </listRelation> <tutelles><tutelle active="#struct-378064" type="direct"><org type="institution" xml:id="struct-378064" status="INCOMING"> <orgName>University of Pittsburgh</orgName> <desc> <address> <country key="FR"></country> </address> </desc> </org></tutelle></tutelles></hal:affiliation><country>États-Unis</country><placeName><settlement type="city">Pittsburgh</settlement><region type="state">Pennsylvanie</region></placeName><orgName type="university">Université de Pittsburgh</orgName></affiliation></author></analytic><idno type="DOI">10.1145/2858036.2858557</idno></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="mix" xml:lang="en"><term> Markov Decision Process</term><term>Inverse Reinforcement Learning</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Human routines are blueprints of behavior, which allow people to accomplish purposeful repetitive tasks at many levels, ranging from the structure of their day to how they drive through an intersection. People express their routines through actions that they perform in the particular situations that triggered those actions. An ability to model routines and understand the situations in which they are likely to occur could allow technology to help people improve their bad habits, inexpert behavior, and other suboptimal routines. However, existing routine models do not capture the causal relationships between situations and actions that describe routines. Our main contribution is the insight that byproducts of an existing activity prediction algorithm can be used to model those causal relationships in routines. We apply this algorithm on two example datasets, and show that the modeled routines are meaningful—that they are predictive of people's actions and that the modeled causal relationships provide insights about the routines that match findings from previous research. Our approach offers a generalizable solution to model and reason about routines.</div></front></TEI><affiliations><list><country><li>France</li><li>États-Unis</li></country><region><li>Pennsylvanie</li></region><settlement><li>Pittsburgh</li></settlement><orgName><li>Université de Pittsburgh</li></orgName></list><tree><country name="États-Unis"><region name="Pennsylvanie"><name sortKey="Banovic, Nikola" sort="Banovic, Nikola" uniqKey="Banovic N" first="Nikola" last="Banovic">Nikola Banovic</name></region><name sortKey="Buzali, Tofi" sort="Buzali, Tofi" uniqKey="Buzali T" first="Tofi" last="Buzali">Tofi Buzali</name><name sortKey="Dey, Anind" sort="Dey, Anind" uniqKey="Dey A" first="Anind" last="Dey">Anind Dey</name><name sortKey="Mankoff, Jennifer" sort="Mankoff, Jennifer" uniqKey="Mankoff J" first="Jennifer" last="Mankoff">Jennifer Mankoff</name></country><country name="France"><noRegion><name sortKey="Chevalier, Fanny" sort="Chevalier, Fanny" uniqKey="Chevalier F" first="Fanny" last="Chevalier">Fanny Chevalier</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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