|Title||How much do you trust me? Learning a case-based model of inverse trust|
|Publication Type||Conference Proceedings|
|Year of Conference||2014|
|Authors||Floyd, MW, Drinkwater, M, Aha, DW|
|Conference Name||Proceedings of the Twenty-Second International Conference on Case-Based Reasoning|
|Conference Location||Cork, Ireland|
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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