CS 451/551: Artificial Intelligence
Course Syllabus -- Spring 2000
Professor: Chris Lynch
Office: 377 Science Center
Office Hours: M 3:00-4:00, WF 3:00-5:00
Course Objective: This course is a comprehensive introduction
to core concepts in artificial intelligence (AI), and surveys active research
areas. Topics covered are:
Text and Software:
- Search strategies: best-first search, heuristics.
- Knowledge representation using predicate logic, semantic networks,
neural nets, frames, and rules.
- Automated deduction: reasoning under uncertainty.
- Applications: problem-solving, planning, expert systems, game playing,
learning, natural language understanding.
- Artificial Intelligence: A Modern Approach , by Russell &
Norvig, Prentice-Hall, 1995. The web page associated with this textbook
is at http://www.cs.berkeley.edu/~russell/aima.html
- ANSI Common LISP , by Paul Graham, Prentice-Hall, 1995.
- Allegro Common LISP (acl) will be used throughout the semester for
the programming assignments. There is a freeware version of this available
- OTTER will be used for automated reasoning assignments. It can be
downloaded at http://www-unix.mcs.anl.gov/AR/otter
- 2 Midterms 50% (tentatively scheduled on 3/1 and 4/12)
- Homework 30%
- Project 20%
The homework assignments may be done in groups of two, and are due in
class on the date posted.
The project allows you to explore in more depth an area of AI, such as
natural language understanding, computer vision, intelligent tutoring
systems, neural networks, or whatever you find most interesting. You may
work in teams of two on a project of sufficient scope, as approved by
the instructor. A brief proposal describing your project is due on Friday,
February 25, and the project itself is due on Friday, April 21. You are
required to demonstrate your project to the instructor during the last
week of class.
Tentative Course Outline
We will do the first three topics in detail, and only touch on the last
- Introduction to AI: The Turing Test; Intelligent Agents. Chapters
1 and 2
- Problem Solving: State-Space Search; Heuristic Search. Game
Playing. Chapters 3,4 & 5
- Knowledge & Reasoning: Predicate Calculus. Inference. Forward
and Backward Chaining. Resolution. Frame Systems and Semantic Nets.
Chapters 6,7,9 & 10
- Planning: Planning Agents. Situation Spaces. Chapter 11
- Reasoning under Uncertainty: Probabilistic Reasoning. Belief
Networks. Decision Making. Chapters 14, 15 & 16
- Learning & Neural Nets: Inductive Learning. Decision Trees.
Feed-forward networks. Back-Propagation. Chapters 18 & 19
- Advanced Topics: Natural Language Understanding. Perception.
Speech Recognition. Chapters 22, 24, & 26
Last modified: 13 Januuary 2000