Modeling and Understanding Human Routine Behavior

Banovic, Nikola; Buzali, Tofi; Chevalier, Fanny; Mankoff, Jennifer; Dey, Anind

Type de document :

Communication dans un congrès avec actes

DOI :

10.1145/2858036.2858557

Titre :

Modeling and Understanding Human Routine Behavior

Auteur(s) :

Banovic, Nikola [Auteur]
Computer Science Department - Carnegie Mellon University
Buzali, Tofi [Auteur]
Computer Science Department - Carnegie Mellon University
Chevalier, Fanny [Auteur]

Computing tools to empower users [MJOLNIR]
Mankoff, Jennifer [Auteur]
Computer Science Department - Carnegie Mellon University
Dey, Anind [Auteur]
Computer Science Department - Carnegie Mellon University

Titre de la manifestation scientifique :

ACM CHI Conference on Human Factors in Computing Systems 2016

Organisateur(s) de la manifestation scientifique :

ACM

Ville :

Santa Clara, California

Pays :

Etats-Unis d'Amérique

Date de début de la manifestation scientifique :

2016-05-07

Date de publication :

2016-05

Mot(s)-clé(s) en anglais :

Inverse Reinforcement Learning
Markov Decision Process

Discipline(s) HAL :

Sciences cognitives/Informatique

Résumé en anglais : [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 ...
Lire la suite >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.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL

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