CS U520 Artificial Intelligence (4 SH)

Provides a basic introduction to AI methods for modeling and implementing intelligent behavior in computers. The focus of the course is on techniques for representing knowledge about the world in formal, computer-understandable languages and creating programs that seek to achieve goals and/or solve problems within these knowledge frameworks. Real-world applications of the techniques presented in the course will be described. The three main approaches to AI studied are: formal logic representations with various forms of automated logical reasoning for solving problems; state space representations with various forms of search for solving problems; the application of machine learning techniques to create systems that can learn from examples and improve their performance with experience. Other topics may include language understanding, planning, computer vision and pattern recognition, uncertain reasoning, knowledge representation, intelligent agents.

CS U213 and PHLU215.

Artificial Intelligence: A Modern Approach , 2nd Edition, by Stuart Russell and Peter Norvig, Prentice Hall, 2003.

Required: I. Search Problem solving through search Heuristic search Application to game playing II. Logical inference Propositional and predicate Logic Techniques for automated inference Application to query answering and/or logic programming III. Machine Learning Computational frameworks for systems that learn Learning from examples - Decision tree learning Neural nets At least three weeks will be devoted to each of the three major areas listed above. Students will have hands-on assignments involving programming all or part of a problem-solving system, setting up problem data, and observing the performance of the system in at least two of the three areas (preferably in all three). Optional topics: Natural language or speech understanding AI planning Knowledge representation and semantic networks Uncertain reasoning models -- Bayesian inference -- Default (non-monotonic) reasoning Perception, Vision and Pattern Recognition using AI Robotics and Applications

Upon completion of this course, a student should be able to: Understand a variety of AI problem-solving frameworks, and their realization as knowledge representation schemes and algorithms Design and implement programs that employ the AI frameworks presented in the course to solve small example problems Know the limitations of AI and what AI methodologies can and cannot do

Midterm, Final Exams and short quizzes Programming homework on implementing some major and basic AI methodologies using common lisp or its equivalent languages for solving problems. A term project (possibly in groups) where students pursue a sub-topic related to the material presented in the course.