A heuristic approach to inductive inference in fact retrieval systems

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Abstract
Heuristic procedures are presented which have been developed to perform inferences by generalizing from available information. The procedures make use of a similarity structure which is imposed on the data base using nonnumerical clustering algorithms. They are implemented in a model fact retrieval system which uses a formal query language and a property-list data structure. A program of experiments is described wherein the procedures are used with test data bases which are altered by deleting part of the data and by purposely introducing false data. It is found that the system can infer the correct response under a variety of conditions involving incomplete and inconsistent data.

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