Incorporating Emotion Perception into Opponent Modeling for Social Dilemmas (bibtex)
by Rens Hoegen, Giota Stratou, Jonathan Gratch
Abstract:
Many everyday decisions involve a social dilemma: cooperation can enhance joint gains, but also make one vulnerable to exploitation. Emotion and emotional signaling is an important element of how people resolve these dilemmas. With the rise of a⬚ective computing, emotion is also an important element of how people resolve these dilemmas with machines. In this article, we learn a predictive model of how people make decisions in an iterative social dilemma. We further show that model accuracy improves by incorporating a player's emotional displays as input to this model, and provide some insight into which emotions in uence social decisions. Finally, we show how this model can be used to perform \textbackslashsocial planning": i.e., to generate a sequence of actions and expressions that achieve social goals (such as maximizing individual rewards). These techniques can be used to enhance machine-understanding of human behavior, as social decision-aids, or to drive the actions of virtual and robotic agents.
Reference:
Incorporating Emotion Perception into Opponent Modeling for Social Dilemmas (Rens Hoegen, Giota Stratou, Jonathan Gratch), In Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems, International Foundation for Autonomous Agents and Multiagent Systems, 2017.
Bibtex Entry:
@inproceedings{hoegen_incorporating_2017,
	address = {Sao Paulo, Brazil},
	title = {Incorporating {Emotion} {Perception} into {Opponent} {Modeling} for {Social} {Dilemmas}},
	url = {http://dl.acm.org/citation.cfm?id=3091239},
	abstract = {Many everyday decisions involve a social dilemma: cooperation can enhance joint gains, but also make one vulnerable to exploitation. Emotion and emotional signaling is an important element of how people resolve these dilemmas. With the rise of a⬚ective computing, emotion is also an important element of how people resolve these dilemmas with machines. In this article, we learn a predictive model of how people make decisions in an iterative social dilemma. We further show that model accuracy improves by incorporating a player's emotional displays as input to this model, and provide some insight into which emotions in
uence social decisions. Finally, we show how this model can be used to perform {\textbackslash}social planning": i.e., to generate a sequence of actions and expressions that achieve social goals (such as maximizing individual rewards). These techniques can be used to enhance machine-understanding of human behavior, as social decision-aids, or to drive the actions of virtual and robotic agents.},
	booktitle = {Proceedings of the 16th {Conference} on {Autonomous} {Agents} and {MultiAgent} {Systems}},
	publisher = {International Foundation for Autonomous Agents and Multiagent Systems},
	author = {Hoegen, Rens and Stratou, Giota and Gratch, Jonathan},
	month = may,
	year = {2017},
	keywords = {Virtual Humans, UARC},
	pages = {801--809}
}
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