Abstract
An important consideration in human-robot teams is ensuring that the robot is trusted by its teammates. Without adequate trust, the robot may be underutilized or disused, potentially exposing human teammates to dangerous situations. We have previously investigated an agent that can assess its own trustworthiness and adapt its behavior accordingly. In this paper we extend our work by adding a transparency layer that allows the agent to explain why it adapted its behavior. The agent uses explanations based on explicit feedback received from an operator. This allows it to provide simple, concise, and understandable explanations. We evaluate our system on scenarios from a simulated robotics domain by demonstrating that the agent can provide explanations that closely align with an operator’s feedback.
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Notes
- 1.
For the remainder of this paper, we use the term robot to refer to a physical (or simulated) robot and agent to refer to the intelligent agent controlling the robot.
- 2.
The case base described in [1] labelled Patrol Random. It contains cases learned from both the speed-focused and detection-focused operators (25 total cases).
- 3.
The learned feedback base is identical to the feedback base described in [2] where feedback is given by the operator 100 % of the time. It contains feedback from both operators.
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Acknowledgements
Thanks to ONR for sponsoring this research. Thanks also to Michael Drinkwater for his assistance in developing the eBotworks scenarios we used to evaluate our agent, and to the reviewers for their comments.
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Floyd, M.W., Aha, D.W. (2016). Incorporating Transparency During Trust-Guided Behavior Adaptation. In: Goel, A., DÃaz-Agudo, M., Roth-Berghofer, T. (eds) Case-Based Reasoning Research and Development. ICCBR 2016. Lecture Notes in Computer Science(), vol 9969. Springer, Cham. https://doi.org/10.1007/978-3-319-47096-2_9
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