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Hierarchical decision models are a general decision support methodology aimed at the classification or evaluation of options that occur in decision-making processes. They are also important for the analysis, simulation and explanation of options. Decision models are typically developed through the decomposition of complex decision problems into smaller and(More)
We present a new machine learning method that, given a set of training examples, induces a definition of the target concept in terms of a hierarchy of intermediate concepts and their definitions. This effectively decomposes the problem into smaller, less complex problems. The method is inspired by the Boolean function decomposition approach to the design of(More)
We present a new machine learning method that, given a set of training examples, induces a definition of the target concept in terms of a hierarchy of intermediate concepts and their definitions. This effectively decomposes the problem into smaller, less complex problems. The method is inspired by the Boolean function decomposition approach to the design of(More)
While not explicitly intended for feature transformation, some methods for switching circuit design implicitly deal with this problem. Given a tabulated Boolean function, these methods construct a circuit that implements that function. In 1950s and 1960s, Ashenhurst [1] and Curtis [2] proposed a function decomposition method that develops a switching(More)
A decision making approach which combines multi-attribute decision making techniques with expert systems is described in the paper. In this approach, knowledge about a particular decision making problem is represented in the form of tree-structured criteria and decision rules. Decision making is supported by an expert system shell. In the paper, the(More)