This course will discuss the key concepts and techniques behind the Knowledge-Based Systems that are the focus of such wide interest today. These systems are at the applied edge of research in Artificial Intelligence. To put them in perspective this course will take a short historical tour through the AI field and its related subtopics. This tour will focus on underlying themes, with examples drawn from representative systems.

The key factors that underly knowledge-based systems are knowledge acquisition, knowledge representation, and the application of large bodies of knowledge to the particular problem domain in which the knowledge-based system operates. Dr. Smith will discuss a number of formalisms for knowledge representation and inference that have been developed to aid in this process. Once again, this will be illustrated with examples drawn from existing systems.

The course will conclude with a discussion of the pragmatics of actually building a knowledge-based system. This will include: (1) suggestions for selecting problems that are amenable to the knowledge-based system approach, and (2) a description of the characteristics of software tools and high-level programming environments that are useful, and for most purposes necessary, for the construction of a practical knowledge-based system.

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