In order for undergraduate teaching faculty to be capable and comfortable in introducing courses in artificial intelligence (AT) and expert systems into the curriculum, they must be well founded in content and have personally experienced prototypical development processes in AT. In addition they need to know the associated learning strategies and experiences of those who have taught and researched this area. This project is presenting an intensive, three week, Summer Institute for 25 faculty consisting of content modules, hands-on laboratory experiments, and teaching strategy sessions to present up-to-date information on Symbolic and Logic Programming, Fundamentals of Artificial Intelligence, and Knowledge-Based and Expert Systems. The fundamental concepts of symbolic programming (as exemplified by the use of Lisp) and of classical logic and logic programming (within the practical limits of Prolog) are being presented first in both a theoretical and a practical laboratory setting. The second week is covering the fundamental paradigms of artificial intelligence (reasoning, goal reduction, constraint propagation, searching, control, deduction, knowledge representation, and the elements of machine learning, vision, robotics, natural language processing). During the third week the participants are learning knowledge engineering concepts and practicing them, working with and gaining experience in programming, analyzing, and using a number of KBS and ES shells in laboratories, and designing a working rule-based system of their choice. The highly modular nature of this summer institute is offering the attending faculty a wide variety of individually tailored courses adaptable to the precise needs and circumstances of their home institution curriculum environment.
