Article

A model of emotions for situated agents

Authors:

John SauterAuthors Info & Claims

AAMAS '06: Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems

Pages 993 - 995

https://doi.org/10.1145/1160633.1160810

Published: 08 May 2006 Publication History

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Abstract

In stressful situations such as combat, emotion is at least as important as rational analysis in determining behavior. DETT (Disposition, Emotion, Trigger, Tendency) is a model of emotion for situated agents that captures the essential features of the OCC (Ortony, Clore, Collins) model in a computationally efficient framework. We motivate and describe this architecture, and report preliminary experiments in simulating combat scenarios.

References

[1]

S. Brave and C. Nass. Emotion in Human-Computer Interaction. In J. A. Jacko and A. Sears, Editors, The human-computer interaction handbook: fundamentals, evolving technologies and emerging applications, pages 81--96. Lawrence Erlbaum Associates, Inc., Mahwah, NJ, 2003.

Digital Library

Google Scholar

[2]

S. Brueckner. Return from the Ant: Synthetic Ecosystems for Manufacturing Control. Thesis at Humboldt University Berlin, Department of Computer Science, 2000.

Google Scholar

[3]

C. Darwin. The expression of the emotions in man and animals. London, John Murray, 1872.

Google Scholar

[4]

J. Gratch and S. Marsella. A Domain-independent Framework for Modeling Emotion. Journal of Cognitive Systems Research, 5(4):269--306, 2004.

Digital Library

Google Scholar

[5]

A. Ilachinski. Artificial War: Multiagent-based Simulation of Combat. Singapore, World Scientific, 2004.

Digital Library

Google Scholar

[6]

A. Kott. Real-Time Adversarial Intelligence & Decision Making (RAID). 2004. http://dtsn.darpa.mil/ixo/programdetail.asp?progid=57.

Google Scholar

[7]

M. K. Lauren and R. T. Stephen. Map-Aware Non-uniform Automata (MANA)---A New Zealand Approach to Scenario Modelling. Journal of Battlefield Technology, 5(1 (March)):27ff, 2002.

Google Scholar

[8]

A. Ortony, G. L. Clore, and A. Collins. The cognitive structure of emotions. Cambridge, UK, Cambridge University Press, 1988.

Crossref

Google Scholar

[9]

H. V. D. Parunak, S. Brueckner, R. Matthews, J. Sauter, and S. Brophy. Characterizing and Predicting Agents via Multi-Agent Evolution. Altarum Institute, Ann Arbor, MI, 2005. http://www.altarum.net/~vparunak/BEE.pdf.

Google Scholar

[10]

H. V. D. Parunak, S. Brueckner, and R. Savit. Universality in Multi-Agent Systems. In Proceedings of Third International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2004), pages 930--937, IEEE, 2004.

Digital Library

Google Scholar

[11]

H. V. D. Parunak and S. A. Brueckner. Extrapolation of the Opponent's Past Behaviors. In A. Kott and W. McEneany, Editors, Adversarial Reasoning: Computational Approaches to Reading the Opponent's Mind, Chapman and Hall/CRC, 2006.

Google Scholar

[12]

H. V. D. Parunak, S. A. Brueckner, R. Matthews, and J. Sauter. Pheromone Learning for Self-Organizing Agents. IEEE SMC, 35(3 (May)):316--326, 2005.

Digital Library

Google Scholar

[13]

R. W. Picard. Affective Computing. Cambridge, MA, MIT Press, 2000.

Digital Library

Google Scholar

[14]

A. S. Rao and M. P. Georgeff. Modeling Rational Agents within a BDI Architecture. In Proceedings of International Conference on Principles of Knowledge Representation and Reasoning (KR-91), pages 473--484, Morgan Kaufman, 1991.

Google Scholar

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Bourgais MTaillandier PVercouter L(2022)BEN : Une architecture pour des agents cognitifs, affectifs et sociaux dans la simulationRevue Ouverte d'Intelligence Artificielle10.5802/roia.413:5-6(501-525)Online publication date: 22-Nov-2022
https://doi.org/10.5802/roia.41
Smith GCarette J(2022)What Lies Beneath—A Survey of Affective Theory Use in Computational Models of EmotionIEEE Transactions on Affective Computing10.1109/TAFFC.2022.319745613:4(1793-1812)Online publication date: 1-Oct-2022
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Published In

AAMAS '06: Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems

May 2006

1631 pages

ISBN:1595933034

DOI:10.1145/1160633

General Chairs:
Hideyuki Nakashima
Future University - Hakodate, Japan
,
Michael Wellman
University of Michigan
,
Program Chairs:
Gerhard Weiss
Technical University Munich, Germany
,
Peter Stone
The University of Texas at Austin

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 May 2006

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AAMAS06

Sponsor:

IFMAS
SIGAI
ATAL

AAMAS06: AAMAS '06 - 5th International Joint Conference on Autonomous Agents and Multi-agent Systems 2006

May 8 - 12, 2006

Japan, Hakodate

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Overall Acceptance Rate 1,155 of 5,036 submissions, 23%

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Cited By

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Bourgais MTaillandier PVercouter L(2022)BEN : Une architecture pour des agents cognitifs, affectifs et sociaux dans la simulationRevue Ouverte d'Intelligence Artificielle10.5802/roia.413:5-6(501-525)Online publication date: 22-Nov-2022
https://doi.org/10.5802/roia.41
Smith GCarette J(2022)What Lies Beneath—A Survey of Affective Theory Use in Computational Models of EmotionIEEE Transactions on Affective Computing10.1109/TAFFC.2022.319745613:4(1793-1812)Online publication date: 1-Oct-2022
https://doi.org/10.1109/TAFFC.2022.3197456
Rato DPrada R(2021)Towards Social Identity in Socio-Cognitive AgentsSustainability10.3390/su13201139013:20(11390)Online publication date: 15-Oct-2021
https://doi.org/10.3390/su132011390
Sánchez-López YCerezo E(2019)Designing emotional BDI agents: good practices and open questionsThe Knowledge Engineering Review10.1017/S026988891900012234Online publication date: 17-Dec-2019
https://doi.org/10.1017/S0269888919000122
Bourgais MTaillandier PVercouter L(2019)BEN: An Agent Architecture for Explainable and Expressive Behavior in Social SimulationExplainable, Transparent Autonomous Agents and Multi-Agent Systems10.1007/978-3-030-30391-4_9(147-163)Online publication date: 11-Sep-2019
https://doi.org/10.1007/978-3-030-30391-4_9
Lejmi-Riahi HBelhaj MBen Said L(2019)Studying Emotions at Work Using Agent-Based Modeling and SimulationArtificial Intelligence Applications and Innovations10.1007/978-3-030-19823-7_48(571-583)Online publication date: 12-May-2019
https://doi.org/10.1007/978-3-030-19823-7_48
Urban GAdamatti D(2018)An Adaptive Agent Approach Using Personality and Emotions2018 XLIV Latin American Computer Conference (CLEI)10.1109/CLEI.2018.00034(215-223)Online publication date: Oct-2018
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Bourgais MTaillandier PVercouter L(2018)Enhancing the Behavior of Agents in Social Simulations with Emotions and Social RelationsMulti-Agent Based Simulation XVIII10.1007/978-3-319-91587-6_7(89-104)Online publication date: 13-May-2018
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Gluz JJaques P(2017)A probabilistic formalization of the appraisal for the OCC event-based emotionsJournal of Artificial Intelligence Research10.5555/3176764.317677958:1(627-664)Online publication date: 1-Jan-2017
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