CS3310 Artificial Intelligence

Survey of topics and methods of Artificial Intelligence. Methods include rule-based systems, heuristic search and exploitation of natural constraints, means-ends analysis, semantic networks, and frames. Emphasis is placed on solving problems that seem to require intelligence rather than attempting to simulate or study natural intelligence. Projects to illustrate basic concepts are assigned. 

Prerequisite

One college-level course in programming.

Lecture Hours

4

Lab Hours

1

Course Learning Outcomes

Upon completion of this course the student is expected to:

• Understand the fifty most common terms in artificial intelligence.
• Be able to write inference rules in Prolog (or equivalent language) for a range of applications.
• Be able to implement small (25 lines or so) artificial-intelligence applications.
• Be able to simulate the key algorithms of artificial intelligence, including backward chaining, automatic backtracking, forward chaining, construction of decision trees, combination of uncertain evidence, depth-first search with heuristics, A* search, minimax search, and means-ends analysis.
• Be able, given an application, to identify the key artificial-intelligence techniques that may apply.