CS 451: Artificial Intelligence
Course Syllabus -- Spring 1998

Professor: Janice Searleman
Office: 370 Science Center
Phone: 268-2377
E-mail: jets@sun.mcs.clarkson.edu

Office Hours: M 2:00-3:00; W,F 11-12:00; Tu,Th 1:00-2:00 and by appointment

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/.
  • Course handouts and assignments will be posted on the public directory ~jets/public/cs451.
Grading Policy:
  • 2 Midterms 30% (tentatively scheduled on 2/26 and 4/9)
  • Final Exam 30%
  • Homework & Quizzes 20%
  • Projects 20%

The written homework assignments are expected to be individual efforts, and are due in class on the date posted. Each day late will result in 10% of the total points deducted. No assignment will be accepted more than 4 days late. However, each student will have a total of 7 days of automatic extensions, to be distributed as needed throughout the semester (to accommodate travel, illness, the demands of other courses, and so on). You are encouraged to budget your free late days as they must last the entire semester. Unused late days will result in bonus points added to your final grade.

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 Wednesday, February 25th, and the project itself is due on Wednesday, April 29th. You are required to demonstrate your project to the instructor during finals week.

Tentative Course Outline

  • Introduction to AI: The Turing Test; Intelligent Agents. Chapters 1 and 2
  • Problem Solving: 1/24-2/12 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. Sityuation 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: 2 February 1998
jets@sun.mcs.clarkson.edu