In situ bidirectional human-robot value alignment
Science Robotics, 2022. [All Versions]. [Preprint]. This paper proposes an explainable artificial intelligence (XAI) system in which a group of robots predicts users’ values by taking in situ feedback into consideration while communicating their decision processes to users…
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A tale of two explanations: Enhancing human trust by explaining robot behavior science.org
Science Robotics, 2019. [All Versions]. [Preprint]. The ability to provide comprehensive explanations of chosen actions is a hallmark of intelligence. Lack of this ability impedes the general acceptance of AI and robot systems in critical tasks. This paper examines what forms…
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Tasks for aligning human and machine planning sciencedirect.com
Current Opinion in Behavioral Sciences, 2019. [All Versions].
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X-ToM: Explaining with Theory-of-Mind for Gaining Justified Human Trust arxiv.org
CVPR XAI Workshop'19, 2019. [All Versions]. This work presents a new explainable AI (XAI) framework aimed at increasing justified human trust and reliance in the AI machine through explanations. The authors pose explanation as an iterative communication process, i.e. dialog…
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Exploration: from machines to humans sciencedirect.com
Current Opinion in Behavioral Sciences, 2020. [All Versions].
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Cognitive Science as a Source of Forward and Inverse Models of Human Decisions for Robotics and Control cocosci.princeton.edu
Annual Review of Control, Robotics, and Autonomous Systems, 2022. [All Versions]. The review focuses on how cognitive science can provide forward models of human decision-making and inverse models of how humans think about others’ decision-making. The authors highlight relevant…
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Bayesian modeling of human–AI complementarity pnas.org
Proceedings of the National Academy of Sciences, 2022. [All Versions]. A Bayesian framework for combining the predictions and different types of confidence scores from humans and machines.
AGI & CoCoSci › Papers > Communications > Coordination: “Science Robotics, 2022. [All Versions]. [Preprint]. This paper proposes an explainable artificial intelligence (XAI) system in which a group of robots predicts users’ values by taking in situ feedback into consideration while communicating their decision processes to users…”