ICS 171: An Introduction to Artificial Intelligence

Course Goals:

Learn the basic AI techniques, the problems for which they are applicable and their limitations. Topics covered include search (solving puzzles, playing games), planning, logical inference (drawing conclusions from data), expert systems, natural language processing and machine learning


Option 1: Homework 25%, Midterm 35%, Final Exam 40%. The final will have one question similar to questions on the midterm exam, and the remaining questions from the last half of the class. Option 2: Final Exam 100%

There will be several extra credit problems offered. Successfully completing one extra credit problem will add up to 2 points to your final grade. You may do two extra credit problems and add up to 4 points to your grade. You may also make up your own extra credit problem but ask for permission first. You must work individually on all extra credit assignments. You can hand them in anytime before the final exam.


Read ics.171 for announcements, answers to homework etc. Also, please post questions about homework or anything else there. If you don't understand something, others probably don't either and will have the same question.


  • FTP Lisp Programs Used in 171

    Lecture Notes

    Select the Browse Notes links for viewing notes, and the postscript for printing notes. The postscript for lecture notes doesn't always display perfectly due to some missing fonts, but it prints fine on ICS laser printers from UNIX. You can try printing them from Macs with DropPs, but the printers are slow.

  • Lecture 1: Introduction

  • Lecture 2: State Space Search

  • Lecture 3: State Space Search 2

  • Lecture 4: Blind Search

  • Lecture 5: Blind Search 2

  • Lecture 6: Heursitic Search

  • Lecture 7: Best First Search

  • Lecture 8: Minimax

  • Lecture 9: Unification and Resolution

  • Lecture 10: Logic Programming

  • Lecture 11: Logic and Lists

  • Lecture 12: Experts Systems

  • Lecture 13: Certainty Factors

  • Lecture 14: Natural Language Processing

  • Lecture 15: Question Answering

  • Lecture 16: Machine Learning

  • Lecture 17: Rule Learning & Neural Nets

    Michael Pazzani.
    Department of Information and Computer Science,
    University of California, Irvine
    Irvine, CA 92717-3425
    Last modified: April 8, 1995