A Nearest-Neighbor Approach to Recognizing Subjective Beliefs in Human-Robot Interaction (bibtex)
by David V. Pynadath, Ning Wang, Ericka Rovira, Michael J. Barnes
Abstract:
Trust is critical to the success of human-robot interaction (HRI), and one of the critical antecedents to trust is transparency. To best interact with human teammates, a robot must be able to ensure that they understand its decision-making process. Recent work has developed automated explanation methods that can achieve this goal. However, individual differences among human teammates require that the robot dynamically adjust its explanation strategy based on their unobservable subjective beliefs. We therefore need methods by which a robot can recognize its teammates’ subjective beliefs relevant to trust-building (e.g., their understanding of the robot’s capabilities and process). We leverage a nonparametric method, common across many fields of artificial intelligence, to enable a robot to use its history of prior interactions as a means for recognizing and predicting a new teammate’s subjective beliefs. We first gather data combining observable behavior sequences with surveybased observations of typically unobservable subjective beliefs. We then use a nearest-neighbor approach to identify the prior teammates most similar to the new one. We use these neighbors to infer the likelihood of possible subjective beliefs, and the results provide insights into the types of subjective beliefs that are easy (and hard) to infer from purely behavioral observations.
Reference:
A Nearest-Neighbor Approach to Recognizing Subjective Beliefs in Human-Robot Interaction (David V. Pynadath, Ning Wang, Ericka Rovira, Michael J. Barnes), In Proceedings of The AAAI Workshop on Plan, Activity, and Intent Recognition (PAIR), Association for the Advancement of Artificial Intelligence, 2018.
Bibtex Entry:
@inproceedings{pynadath_nearest-neighbor_2018,
	address = {London, UK},
	title = {A {Nearest}-{Neighbor} {Approach} to {Recognizing} {Subjective} {Beliefs} in {Human}-{Robot} {Interaction}},
	url = {https://aied2018.utscic.edu.au/proceedings/},
	abstract = {Trust is critical to the success of human-robot interaction (HRI), and one of the critical antecedents to trust is transparency. To best interact with human teammates, a robot must be able to ensure that they understand its decision-making process. Recent work has developed automated explanation methods that can achieve this goal. However, individual differences among human teammates require that the robot dynamically adjust its explanation strategy based on their unobservable subjective beliefs. We therefore need methods by which a robot can recognize its teammates’ subjective beliefs relevant to trust-building (e.g., their understanding of the robot’s capabilities and process).
We leverage a nonparametric method, common across many fields of artificial intelligence, to enable a robot to use its history of prior interactions as a means for recognizing and predicting a new teammate’s subjective beliefs. We first gather data combining observable behavior sequences with surveybased observations of typically unobservable subjective beliefs. We then use a nearest-neighbor approach to identify the prior teammates most similar to the new one. We use these neighbors to infer the likelihood of possible subjective beliefs, and the results provide insights into the types of subjective beliefs that are easy (and hard) to infer from purely behavioral observations.},
	booktitle = {Proceedings of {The} {AAAI} {Workshop} on {Plan}, {Activity}, and {Intent} {Recognition} ({PAIR})},
	publisher = {Association for the Advancement of Artificial Intelligence},
	author = {Pynadath, David V. and Wang, Ning and Rovira, Ericka and Barnes, Michael J.},
	month = jun,
	year = {2018},
	keywords = {ARL, DoD, Social Simulation, UARC}
}
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