Discovering Hidden Dispositions and Situational Factors in Causal Relations by Means of Contextual Independencies
نویسنده
چکیده
Correspondent inferences in attribution theory deal with assigning causes to behaviour based on true dispositions rather than situational factors. In this paper, we investigate how knowledge representation tools in Artificial Intelligence (AI), such as Bayesian networks (BNs), can help represent such situations and distinguish between the types of clues used in assessing the behaviour (dispositional or situational). We also demonstrate how a discovery algorithm for contextual independencies can provide the information needed to separate a seemingly erroneous causal model (considering dispositions and situations together) into two more accurate models, one for dispositions and one for situations.
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