Methods of representing knowledge for automated inference and decision making are covered. Emphasis is placed on representations of time, space, causality, action, belief and uncertainty. Methods covered will include propositional, description, first-order, modal and nonmonotonic logics; Bayesian networks; probabilistic context-free grammars; scripts and frames. Emphasis is placed on the connections and differences between methods, their strengths and weaknesses and gaining experience in using them to effectively represent knowledge.