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Transcript
Brooklyn College
Department of Computer and Information Science
CISC 3410 [32] Artificial Intelligence
3 hours; 3 credits
Techniques for making computers exhibit intelligent behavior. Topics covered are taken from the
areas of problem solving, perception, game playing, knowledge representation, natural language
understanding, programs that learn (adaptive programs), expert systems, and programming
languages for work in artificial intelligence.
Objective
To introduce the students to some of the basic theory and practical techniques in artificial
intelligence. In particular, this course teaches about:
1. Search
2. Knowledge representation
3. Machine learning
4. Planning
5. Software agents
Syllabus
1. Introduction
a. What is intelligence?
b. The history of artificial intelligence
c. Intelligent agents
2. Search
a. State space representation
b. Depth and breath-first search
c. Heuristic search
d. Adversarial search & Game playing
3. Machine Learning
a. Neural networks
b. Evolutionary computation
c. Reinforcement learning
4. Knowledge Representation
a. Production rules
b. Frames & Semantic networks
c. Propositional logic
d. Predicate logic
5. Planning
a. STRIPS & Linear planning
b. Partial-order planning
c. Decision-theoretic planning
Textbook
Stuart Russell and Peter Norvig. Artificial Intelligence: A Modern Approach (second edition), ISBN
0-13-790395-2, Prentice Hall.