Modeling and Understanding Human Routine Behavior
Nikola Banovic,Tofi Buzali,Fanny Chevalier,Jennifer Mankoff,Anind K. Dey +4 more
- pp 248-260
Reads0
Chats0
TLDR
The main contribution is the insight that byproducts of an existing activity prediction algorithm can be used to model those causal relationships in routines and show that the modeled routines are meaningful-that they are predictive of people's actions and that the Models provide insights about the routines that match findings from previous research.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.read more
Citations
More filters
Journal ArticleDOI
UTiLearn: A Personalised Ubiquitous Teaching and Learning System for Smart Societies
TL;DR: A personalised Ubiquitous eTeaching & eLearning (UTiLearn) framework that leverages Internet of Things, big data, supercomputing, and deep learning to provide enhanced development, management, and delivery of teaching and learning in smart society settings is proposed.
Journal ArticleDOI
Digital Behaviour Change Interventions to Break and Form Habits
TL;DR: This work critically review the main theories and models used in the research to analyse their application to designing effective habitual behaviour change interventions, and synthesises these theories into an explanatory framework, the Habit Alteration Model, and outlines the state of the art.
BookDOI
The Cambridge Handbook of Computing Education Research
TL;DR: The Computer Education Handbook as mentioned in this paper describes the extent and shape of computing education research today and provides an authoritative introduction to the field and is essential reading for policy makers, as well as both new and established researchers.
Proceedings ArticleDOI
Do You Want Your Autonomous Car To Drive Like You
TL;DR: It is found that users tend to prefer a significantly more defensive driving style than their own, even though their actual driving style tends to be more aggressive.
References
More filters
Journal ArticleDOI
Information Theory and Statistical Mechanics. II
TL;DR: In this article, the authors consider statistical mechanics as a form of statistical inference rather than as a physical theory, and show that the usual computational rules, starting with the determination of the partition function, are an immediate consequence of the maximum-entropy principle.
Book
Markov Decision Processes: Discrete Stochastic Dynamic Programming
TL;DR: Puterman as discussed by the authors provides a uniquely up-to-date, unified, and rigorous treatment of the theoretical, computational, and applied research on Markov decision process models, focusing primarily on infinite horizon discrete time models and models with discrete time spaces while also examining models with arbitrary state spaces, finite horizon models, and continuous time discrete state models.
Journal ArticleDOI
Understanding and Using Context
TL;DR: An operational definition of context is provided and the different ways in which context can be used by context-aware applications are discussed, including the features and abstractions in the toolkit that make the task of building applications easier.
Journal ArticleDOI
Reconceptualizing Organizational Routines as a Source of Flexibility and Change
TL;DR: The authors argue that the relationship between ostensive and performative aspects of routines creates an on-going opportunity for variation, selection, and retention of new practices and patterns of action within routines and allows routines to generate a wide range of outcomes, from apparent stability to apparent stability.
Proceedings ArticleDOI
Apprenticeship learning via inverse reinforcement learning
Pieter Abbeel,Andrew Y. Ng +1 more
TL;DR: This work thinks of the expert as trying to maximize a reward function that is expressible as a linear combination of known features, and gives an algorithm for learning the task demonstrated by the expert, based on using "inverse reinforcement learning" to try to recover the unknown reward function.