Abstract
Robots can be important additions to human teams if they improve team performance by providing new skills or improving existing skills. However, to get the full benefits of a robot the team must trust and use it appropriately. We present an agent algorithm that allows a robot to estimate its trustworthiness and adapt its behavior in an attempt to increase trust. It uses case-based reasoning to store previous behavior adaptations and uses this information to perform future adaptations. We compare case-based behavior adaptation to behavior adaptation that does not learn and show it significantly reduces the number of behaviors that need to be evaluated before a trustworthy behavior is found. Our evaluation is in a simulated robotics environment and involves a movement scenario and a patrolling/threat detection scenario.
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Floyd, M.W., Drinkwater, M., Aha, D.W. (2014). How Much Do You Trust Me? Learning a Case-Based Model of Inverse Trust. In: Lamontagne, L., Plaza, E. (eds) Case-Based Reasoning Research and Development. ICCBR 2014. Lecture Notes in Computer Science(), vol 8765. Springer, Cham. https://doi.org/10.1007/978-3-319-11209-1_10
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DOI: https://doi.org/10.1007/978-3-319-11209-1_10
Publisher Name: Springer, Cham
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