Presented are innovative teaching and learning techniques for the teaching of knowledge-based paradigms. The main knowledge-based intelligent paradigms are expert systems, artificial neural networks, fuzzy systems and evolutionary computing. Expert systems are designed to mimic the performance of biological systems. Artificial neural networks can mimic the biological information processing mechanism in a very limited sense. Evolutionary computing algorithms are used for optimization applications, and fuzzy logic provides a basis for representing uncertain and imprecise knowledge.

Useful help to develop successful learning and teaching tools for the teaching of knowledge-based paradigms

Inhalt
D. Tedman, L.C. Jain: An Introduction to Innovative Teaching and Learning.- R.S.T. Lee, J.N.K. Liu: Teaching and Learning the AI Modeling.- C.L. Karr, C. Sunal, C. Smith: Artificial Intelligence Techniques for an Interdisciplinary Science Course.- J.F. Vega-Riveros: On the Architecture of Intelligent Tutoring Systems and its Application to a Neural Networks Course.- V. Devedzic: Teaching Knowledge Modeling at the Graduate Level - a Case Study.- V. Devedzic, D. Radovic, L. Jerinic: Innovative Modeling Techniques for Intelligent Tutoring Systems.- J. Fulcher: Teaching Course on Artificial Neural Networks.- T. Hiyama: Innovative Education for Fuzzy Logic Stabilization of Electric Power Systems in a Matlab/Simulink Environment.- W.L. Goh, S.K. Amarasinghe: A Neural Network Wokbench for Teaching and Learning.- C.A. Higgins, F.Z. Mansouri: PRAM: A Courseware System for the Automatic Assessment of AI Programs.