Identifying the Right Reasons: Learning to Filter Decision Makers

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Given a domain of related problem classes and a set of general decision-making procedures applicable to them, this paper describes AWL, an algorithm to filter out those procedures that prove irrelevant, selfcontradictory, or untrustworthy for a particular class. With an external model of expertise as its performance criterion, the algorithm uses a perceptron-like model to learn problem-class-specific weights for its decision-making procedures. Learning improves both the efficiency of the decision-making process and the performance of the system.