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Computational Interaction$
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Antti Oulasvirta, Per Ola Kristensson, Xiaojun Bi, and Andrew Howes

Print publication date: 2018

Print ISBN-13: 9780198799603

Published to Oxford Scholarship Online: March 2018

DOI: 10.1093/oso/9780198799603.001.0001

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PRINTED FROM OXFORD SCHOLARSHIP ONLINE (oxford.universitypressscholarship.com). (c) Copyright Oxford University Press, 2021. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in OSO for personal use. date: 19 October 2021

Computational Model of Human Routine Behaviours

Computational Model of Human Routine Behaviours

(p.377) 14 Computational Model of Human Routine Behaviours
Computational Interaction

Nikola Banovic

Jennifer Mankoff

Anind K. Dey

Oxford University Press

Computational Interaction enables a future in which user interfaces (UI) learn about people’s behaviours by observing them and interacting with them to help people to be productive, comfortable, healthy, and safe. However, this requires technology that can accurately model people’s behaviours. This chapter focuses on human routine behaviours enacted by people as sequences of actions performed in specific situations, i.e. behaviour instances, and presents a probabilistic, generative model of human routine behaviours that can describe, reason about, and act in response to people’s behaviours. We holistically define human routine behaviours to constrain the patterns extracted from the data, match routine behaviours, and estimate the likelihood that people will perform certain actions (in different situations) in a way that matches their demonstrated preference. The chapter illustrates how computational models of routines support stakeholders in making sense of stored logs of human behaviour, and designing UIs that respond to those behaviours.

Keywords:   human behaviour, routine, habits, computational model, Markov decision process, inverse reinforcement learning, human-data supported interfaces

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