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- W1354071 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." @default.
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- W1354071 date "2014-01-01" @default.
- W1354071 modified "2023-09-23" @default.
- W1354071 title "How Much Do You Trust Me? Learning a Case-Based Model of Inverse Trust" @default.
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- W1354071 doi "https://doi.org/10.1007/978-3-319-11209-1_10" @default.
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