Automated Reasoning

3 Credits · Level: Graduate, Undergraduate

This graduate lecture surveys methods of automated deductive reasoning. Through traditional lectures, programming projects, paper presentations, and research projects, students learn (1) to understand the foundations of logical and probabilistic methods of automated reasoning. (2) to implement algorithms for logical and probabilistic reasoning, (3) to comprehend, analyze, and critique papers from the primary literature, (4) to replicate studies described in the primary literature, and (5) to design, conduct, and present their own studies. Topics include propositional logic, predicate logic, resolution proof, production systems, Prolog, uncertain reasoning, certainty factors, Bayesian decision theory, Bayesian networks, exact inference, approximate inference, first-order probabilistic models, probabilistic programming languages, and applications.