Integrating personality and mood with agent emotions

Ojha, S; Vitale, J; Raza, SA; Billingsley, R; Williams, MA

Integrating personality and mood with agent emotions

Ojha, S

Vitale, J

Raza, SA Billingsley, R Williams, MA

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Publisher:: ASSOC COMPUTING MACHINERY
Publication Type:: Conference Proceeding
Citation:: Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS, 2019, 4, pp. 2147-2149
Issue Date:: 2019-01-01

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Field	Value	Language
dc.contributor.author	Ojha, S https://orcid.org/0000-0003-4856-8335
dc.contributor.author	Vitale, J https://orcid.org/0000-0001-6099-675X
dc.contributor.author	Raza, SA
dc.contributor.author	Billingsley, R
dc.contributor.author	Williams, MA
dc.date	2019-05-13
dc.date.accessioned	2020-06-20T01:28:55Z
dc.date.available	2020-06-20T01:28:55Z
dc.date.issued	2019-01-01
dc.identifier.citation	Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS, 2019, 4, pp. 2147-2149
dc.identifier.isbn	9781510892002
dc.identifier.issn	1548-8403
dc.identifier.issn	1558-2914
dc.identifier.uri	http://hdl.handle.net/10453/141575
dc.description.abstract	© 2019 International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS). All rights reserved. An intelligent agent should be able to show different emotional behaviours in different interaction situations to become believable and establish close relationships with human counterparts. It is widely accepted that personality and mood play an important role in modulating emotions. However, current computational accounts of emotion for intelligent agents do not effectively integrate the notions of personality and mood in the process of emotion generation. Previous attempts that have been made are mostly based on the assumptions of the researcher, rather than on empirical data and scientific validation. In this paper, we present the results of a novel supervised machine learning approach used to train a network of emotions that integrates the factors of personality and mood, which provides a high emotion intensity prediction accuracy.
dc.language	en
dc.publisher	ASSOC COMPUTING MACHINERY
dc.relation	http://purl.org/au-research/grants/arc/DP160102693
dc.relation.ispartof	Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
dc.relation.ispartof	18th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS)
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Integrating personality and mood with agent emotions
dc.type	Conference Proceeding
utslib.citation.volume	4
utslib.location.activity	Montreal, CANADA
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Software
utslib.copyright.status	open_access	*
dc.date.updated	2020-06-20T01:28:51Z
pubs.finish-date	2019-05-17
pubs.publication-status	Published
pubs.start-date	2019-05-13
pubs.volume	4

Abstract:

© 2019 International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS). All rights reserved. An intelligent agent should be able to show different emotional behaviours in different interaction situations to become believable and establish close relationships with human counterparts. It is widely accepted that personality and mood play an important role in modulating emotions. However, current computational accounts of emotion for intelligent agents do not effectively integrate the notions of personality and mood in the process of emotion generation. Previous attempts that have been made are mostly based on the assumptions of the researcher, rather than on empirical data and scientific validation. In this paper, we present the results of a novel supervised machine learning approach used to train a network of emotions that integrates the factors of personality and mood, which provides a high emotion intensity prediction accuracy.

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http://hdl.handle.net/10453/141575