An incremental diagnosis algorithm of human erroneous decision making
Abstract
This paper presents an incremental consistency-based diagnosis (CBD) algorithm that studies and provides explanations for erroneous human decision-making. Our approach relies on minimal correction sets to compute belief states that are consistent with the recorded human actions and observations. We demonstrate that our incremental algorithm is correct and complete wrt classical CBD. Moreover, it is capable of distinguishing between different types of human errors that cannot be captured by classical CBD.
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