Abstract
This paper describes ML-SMART, a knowledge based system for inducing conceptual description from examples. It is organized as a problem solver and makes an integrated use of multiple search strategies, including characterization, constructive learning and deduction.
The acquired knowledge consists of production rules organized into a network, which can be seen as a generalization of a decision tree. The condition part of the rules is expressed in a first order logic language also containing numerical quantification. The use of variables and functions allows highly structured concepts to be easily described.
ML-SMART (Similarity-based Multiple-concept Acquisition and Reasoning Tool) is a domain independent system, provided with a friendly interface to ease the task of configuring a new application. It is well suited to work in noisy environments and has been tested in different domains for the sake of comparison with other existing systems.
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