Course Description
Introduction to Artificial Intelligence and Computer Simulation
Spring 2001

Artificial Intelligence is a broad research area that develops computational models of processes such as perception, reasoning and learning. Different investigators focus on different aspects of this problem, pursue them with different goals, and apply widely divergent methods. Some favor an approach based on symbolic computation; others based on neural models, and others on evolutionary processes. This class provides a survey of techniques for machine intelligence and computational modeling of human intelligence.

The class will cover many areas that AI has explored and implement small AI programs for a number of these topics. Topics will include intelligent agents, neural networks, problem-solving and search, genetic algorithms, knowledge representation and reasoning, planning, and machine learning.

The class will examine a number of debates and tradeoffs. As we will see throughout the course, AI is not a field in which there is a set of neatly defined problems to solve, nor one in which it is obvious what constitutes a solution. Consequently, learning about AI involves not only learning about methods but also learning how to ask good questions: developing a viewpoint on what constitutes an AI question, how to define AI questions, and learning critical thinking about the strengths and weaknesses of answers.

The course will include both written and programming assignments. Programming assignments will be done in scheme. The ability to program in scheme or lisp is an essential prerequisite to this course.


Upon completion of this class, you will have a greater appreciation for the issues and difficulties involved in developing intelligent systems and experience in implementing various AI techniques. In addition, you will have some idea of the current state and limitations of artificial intelligence and computational cognitive science.


For questions about the course, please contact David Leake (