CS 441 Artificial Intelligence

Credit Hours: 4
Course Coordinator: Melanie Mitchell
Course Description: Introduction to the basic concepts and techniques of artificial intelligence. Knowledge representation, problem solving, machine learning, natural language understanding, and AI search techniques.
Prerequisites: CS 202

This course will provide students with an overview of the major topics and techniques of current-day artificial intelligence. Upon the successful completion of this course students will be able to:


    1. Describe the major components and applications of intelligent agents.

    2. Describe and implement AI search techniques for heuristic problem-solving and game playing, and describe their strengths and limitations.

    3. Describe and implement various AI knowledge-representation techniques.

    4. Design software agents that use logic to reason.

    5. Design software agents that use Bayesian techniques to learn and reason under uncertainty.

    6. Describe the basic ideas in current-day research on natural-language processing, computer vision, and analogy-making.

    1. 7. Summarize the major philosophical questions regarding AI and the major schools of thought on these questions
Textbooks: Artificial Intelligence, George Luger, 2009
References: None.
Major Topics:

1. Application areas of AI


2. Problem-solving and game-playing as search

3. Knowledge Representation

4. Biologically inspired AI

5. Learning and reasoning under uncertainty

6. Natural-language processing

7. Vision

8. Analogy-making

9. Robotics

10. Philosophy of AI.

Laboratory Exercises:


CAC Category Credits Core Advanced
Data Structures 0.5
Algorithms 1.0
Software Design 0.5
Computer Architecture
Programming Languages


Oral and Written Communications: Students will give oral presentations in class and will write up their final project as a scientific paper.
Social and Ethical Issues: Students will learn about and discuss the social and ethical issues related to the development of artificial intelligence (10% class time)
Theoretical Content: Logic, probability theory and statistics, principal components analysis. (20% class time)
Problem Analysis: Students will analyze a particular set of problems related to implementing an intelligent agent and will develop software to address these problems. (50% class time)
Solution Design: See above.