Knowledge Representation and Inference

Catalog description

This course is designed to introduce students to the principles and practice of representing and reasoning with knowledge for the design of AI systems. The course will cover logical, probabilistic, and decision-theoretic knowledge representations and their applications in query answering, decision making, and planning. Upon successful completion of the course, the students will be able to choose, design and apply appropriate modern knowledge representation and inference techniques to solve AI problems. Topics include logical knowledge representations, probabilistic knowledge representations, decision-theoretic knowledge representations, action representations and their use in planning, concept of ontologies, knowledge graphs, graph alignment and reasoning. Laboratory assignments will be used to provide hands-on experience with knowledge representation-based solutions to real-world AI problems of moderate complexity.