Interactive learning: a multiexpert paradigm for acquiring new knowledge

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Abstract
In this paper a paradigm for knowledge acquisition is presented. The paradigm, referred to as Multiexpert Knowledge System (MKS), is based on the philosophy that many decision problems which are candidate expert system applications do not rely on just a single expert for advice but utilize the expertise of many, sometimes conflicting, knowledge sources. Because of the potential for conflict between sources, the contemporary approach to building multiple expert or multiexpert knowledge systems has been to eliminate conflict prior to building the knowledge base. The MKS paradigm accommodates multiple, potentially conflicting experts and uses their expertise in the formulation of new knowledge (rules). These new rules are tested using sequential analysis and organized into a knowledge base by means of an entropy reduction program. Together the MKS paradigm, sequential analysis and entropy reduction are components in the design of an 'interactive' learning expert system which behaves as a 'virtual' expert learning and unlearning new knowledge.

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