Artificial Intelligence Machine Learning Explanatory AI
Theory and application of artificial intelligence representation and reasoning techniques.
R.G. (Randy) Goebel is currently professor of Computing Science in the Department of Computing Science at the University of Alberta and Fellow and co-founder of the Alberta Machine Intelligence Institute (Amii). He received the B.Sc. (Computer Science), M.Sc. (Computing Science), and Ph.D. (Computer Science) from the Universities of Regina, Alberta, and British Columbia, respectively.
Professor Goebel's theoretical work on abduction, hypothetical reasoning and belief revision is internationally well known; his recent research is focused on the formalization of visualization and explainable artificial intelligence (XAI), especially in applications in autonomous driving, legal reasoning, and precision health. He has worked on optimization, algorithm complexity, systems biology, natural language processing, and automated reasoning.
Randy has previously held faculty appointments at the University of Waterloo, University of Tokyo, Multimedia University (Kuala Lumpur), Hokkaido University (Sapporo), visiting researcher engagements at National Institute of Informatics (Tokyo), DFKI (Germany), and NICTA (now Data61, Australia); he is actively involved in collaborative research projects in Canada, Japan, Germany, France, the UK, and China.
An introduction to the tools of set theory, logic, and induction, and their use in the practice of reasoning about algorithms and programs. Basic set theory; the notion of a function; counting; propositional and predicate logic and their proof systems; inductive definitions and proofs by induction; program specification and correctness. Prerequisites: CMPUT 101, 174, 175, 274, or SCI 100.