Abstract | ||
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This paper develops a model of natural human reasoning with linguistic information. The proposed model is based on a new interpretation for a generalized implication clause with linguistic variables [10, 17]. An implication clause along with the modus ponens rule of inference can be used to model a large class of human decision procedures. Here, an implication is interpreted as a rule of classification. Pattern recognition is basically classification of items into some equivalence classes. Thus, it is possible to develop a pattern recognition interpertation for implication clauses. Using the proposed interpretation, a new approach for automating decision procedures is presented.The new model of reasoning is computationally manageable. Moreover, it has the advantage that it enables application of well-known methods used in pattern recognition and clustering techniques to automate the process of human decision-making. One of these applications is a method of reducing the order of an implication clause, i.e., reducing the number of premises in the implication. This is based on feature selection, which is a stage implemented in many pattern recognition systems.The ideas discussed in this paper have been implemented in an expert system for the computer-aided design of men-machine interface. An overview of that system has appeared [13]. |
Year | DOI | Venue |
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1991 | 10.1016/0020-0255(91)90078-9 | Inf. Sci. |
Keywords | Field | DocType |
pattern recognition interpretation,pattern recognition | Rule-based machine translation,Feature selection,Computer science,Artificial intelligence,Natural language processing,Equivalence class,Cluster analysis,Modus ponens,Pattern recognition,Character recognition,Expert system,Rule of inference,Machine learning | Journal |
Volume | ISSN | Citations |
57-58, | 0020-0255 | 3 |
PageRank | References | Authors |
1.87 | 1 | 3 |
Name | Order | Citations | PageRank |
---|---|---|---|
Dan E. Tamir | 1 | 79 | 13.26 |
Daniel G. Schwartz | 2 | 91 | 17.17 |
Abraham Kandel | 3 | 2145 | 276.03 |