Introduction to Artificial Intelligence
and Computer Simulation
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
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