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An approach to decision making that integrates multi-attribute decision techniques with expert systems is described. The approach is based on the explicit articulation of qualitative decision knowledge which is represented by a tree of attributes and decision rules. The decision making process is supported by a specialized expert system shell for(More)
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)
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)
DEX is an expert system shell for qualitative multi-attribute decision modeling and support. During the last decade, it has been applied over fifty times in complex real-world decision problems. In this article we advocate for the applicability and great potential of this approach for industrial decision-making. The approach is illustrated by a typical(More)
Data mining is often used to develop predictive models from data, but rarely addresses how these models are to be employed. To use the constructed model, the user is usually required to run an often complex data mining suite in which the model has been constructed. A better mechanism for the communication of resulting models and less complex, easy to use(More)