Computer models of emotion inform theories of human intelligence and advance human-centric applications. Processes akin to emotion are required by any intelligent entity facing a dynamic, uncertain, and social environment. Psychological theories of emotion (such as appraisal theory) can serve as an architectural specification for machines that aim to recognize, model, and simulate human affect. Realizing psychological theories as working computational models advances science by forcing concreteness, revealing hidden assumptions, and creating dynamic artifacts that can be subject to empirical study.
%0 Journal Article
%1 MarsellaGratch14cacm
%A Marsella, Stacy
%A Gratch, Jonathan
%D 2014
%J Communications of the ACM
%K 01801 acm paper ai user interface interaction agent emotion zzz.hci zzz.mmi
%N 12
%P 56--67
%R 10.1145/2631912
%T Computationally Modeling Human Emotion
%V 57
%X Computer models of emotion inform theories of human intelligence and advance human-centric applications. Processes akin to emotion are required by any intelligent entity facing a dynamic, uncertain, and social environment. Psychological theories of emotion (such as appraisal theory) can serve as an architectural specification for machines that aim to recognize, model, and simulate human affect. Realizing psychological theories as working computational models advances science by forcing concreteness, revealing hidden assumptions, and creating dynamic artifacts that can be subject to empirical study.
@article{MarsellaGratch14cacm,
abstract = {Computer models of emotion inform theories of human intelligence and advance human-centric applications. Processes akin to emotion are required by any intelligent entity facing a dynamic, uncertain, and social environment. Psychological theories of emotion (such as appraisal theory) can serve as an architectural specification for machines that aim to recognize, model, and simulate human affect. Realizing psychological theories as working computational models advances science by forcing concreteness, revealing hidden assumptions, and creating dynamic artifacts that can be subject to empirical study.},
added-at = {2014-12-13T22:26:11.000+0100},
author = {Marsella, Stacy and Gratch, Jonathan},
biburl = {https://www.bibsonomy.org/bibtex/29d844d3973c6946bcdbacd8f2a3474da/flint63},
doi = {10.1145/2631912},
file = {ACM Digital Library:2014/MarsellaGratch14cacm.pdf:PDF},
groups = {public},
interhash = {91ed83dcda69dc56c488be78b23a2c0e},
intrahash = {9d844d3973c6946bcdbacd8f2a3474da},
issn = {0001-0782},
journal = {Communications of the ACM},
keywords = {01801 acm paper ai user interface interaction agent emotion zzz.hci zzz.mmi},
month = {#dec#},
number = 12,
pages = {56--67},
timestamp = {2018-04-16T12:18:06.000+0200},
title = {Computationally Modeling Human Emotion},
username = {flint63},
volume = 57,
year = 2014
}