A Conceptual Model of Adaptive Knowledge-Based Systems

The ability to learn or adapt is widely recognized as one of the most prominent abilities of any animate or inanimate intelligent system. While considerable progress has been made in the science and technology of machine learning, little of that has been incorporated in traditional knowledge-based systems such as diagnostic or expert systems operating in a managerial environment. In this paper a conceptual model of an adaptive expert system is proposed as an attempt to lay a foundation for building knowledge-based systems that can learn by interacting with the environment. In contrast to existing models for learning such as for knowledge acquisition and skill refinement where the issue of noise and uncertainty is usually neglected, our model incorporates a stochastic environment and a learning response behavior which too is stochastic in nature.

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