CS 451/551: Artificial Intelligence
Course Syllabus -- Spring 1999

Professor: Chris Lynch
Office: 377 Science Center
Phone: 268-2384
E-mail: clynch@clarkson.edu

New Classroom: Science Center 162

Office Hours: T,Th 10:00-11:00, 2:30-4:00

Course Objective: This course is a comprehensive introduction to core concepts in artificial intelligence (AI), and surveys active research areas. Topics covered are:

  • 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.
Text and Software:
  • 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
  • Allegro Common LISP (acl) will be used throughout the semester for the programming assignments. There is a freeware version of this available at http://www.franz.com/.
Grading Policy:
  • 2 Midterms 40% (tentatively scheduled on 2/26 and 4/8)
  • Homework 40%
  • Project 20%

The written homework assignments are expected to be individual efforts, 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 Thursday, February 25th, and the project itself is due on Thursday, April 22nd. You are required to demonstrate your project to the instructor during the last week of class.

Tentative Course Outline

  • 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: 18 Januuary 1999
clynch@clarkson.edu