A Markovian Method for Predicting Trust Behavior in Human-Agent Interaction (bibtex)
by David V. Pynadath, Ning Wang, Sreekar Kamireddy
Abstract:
Trust calibration is critical to the success of human-agent interaction (HAI). However, individual differences are ubiquitous in people’s trust relationships with autonomous systems. To assist its heterogeneous human teammates calibrate their trust in it, an agent must first dynamically model them as individuals, rather than communicating with them all in the same manner. It can then generate expectations of its teammates’ behavior and optimize its own communication based on the current state of the trust relationship it has with them. In this work, we examine how an agent can generate accurate expectations given observations of only the teammate’s trust-related behaviors (e.g., did the person follow or ignore its advice?). In addition to this limited input, we also seek a specific output: accurately predicting its human teammate’s future trust behavior (e.g., will the person follow or ignore my next suggestion?). In this investigation, we construct a model capable of generating such expectations using data gathered in a humansubject study of behavior in a simulated human-robot interaction (HRI) scenario. We first analyze the ability of measures from a presurvey on trust-related traits to accurately predict subsequent trust behaviors. However, as the interaction progresses, this effect is dwarfed by the direct experience. We therefore analyze the ability of sequences of prior behavior by the teammate to accurately predict subsequent trust behaviors. Such behavioral sequences have shown to be indicative of the subjective beliefs of other teammates, and we show here that they have a predictive power as well.
Reference:
A Markovian Method for Predicting Trust Behavior in Human-Agent Interaction (David V. Pynadath, Ning Wang, Sreekar Kamireddy), In Proceedings of the 7th International Conference on Human-Agent Interaction - HAI '19, ACM Press, 2019.
Bibtex Entry:
@inproceedings{pynadath_markovian_2019,
	address = {Kyoto, Japan},
	title = {A {Markovian} {Method} for {Predicting} {Trust} {Behavior} in {Human}-{Agent} {Interaction}},
	isbn = {978-1-4503-6922-0},
	url = {http://dl.acm.org/citation.cfm?doid=3349537.3351905},
	doi = {10.1145/3349537.3351905},
	abstract = {Trust calibration is critical to the success of human-agent interaction (HAI). However, individual differences are ubiquitous in people’s trust relationships with autonomous systems. To assist its heterogeneous human teammates calibrate their trust in it, an agent must first dynamically model them as individuals, rather than communicating with them all in the same manner. It can then generate expectations of its teammates’ behavior and optimize its own communication based on the current state of the trust relationship it has with them. In this work, we examine how an agent can generate accurate expectations given observations of only the teammate’s trust-related behaviors (e.g., did the person follow or ignore its advice?). In addition to this limited input, we also seek a specific output: accurately predicting its human teammate’s future trust behavior (e.g., will the person follow or ignore my next suggestion?). In this investigation, we construct a model capable of generating such expectations using data gathered in a humansubject study of behavior in a simulated human-robot interaction (HRI) scenario. We first analyze the ability of measures from a presurvey on trust-related traits to accurately predict subsequent trust behaviors. However, as the interaction progresses, this effect is dwarfed by the direct experience. We therefore analyze the ability of sequences of prior behavior by the teammate to accurately predict subsequent trust behaviors. Such behavioral sequences have shown to be indicative of the subjective beliefs of other teammates, and we show here that they have a predictive power as well.},
	booktitle = {Proceedings of the 7th {International} {Conference} on {Human}-{Agent} {Interaction}  - {HAI} '19},
	publisher = {ACM Press},
	author = {Pynadath, David V. and Wang, Ning and Kamireddy, Sreekar},
	month = oct,
	year = {2019},
	keywords = {MedVR, Social Simulation, UARC},
	pages = {171--178}
}
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