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Course Outline

  • Professor: Rina Dechter
  • Electronic Mail: dechter@ics.uci.edu
  • Place: ICS 174
  • Time: TuTh 11:00 to 12:20
  • Office: ICS 424E
  • Office Hours: Monday, Thursday, 1:00 to 2:00 pm.
  • Textbooks:
  • Teaching Assistants
  • Discussion Sections
    • 36371 DIS 1: Tue, 2-2:50 in ICS 243
    • 36372 DIS 2: Wed 10-10:50 in ICF 101

    Course Goals:

      Learn the basic AI techniques, the problems for which they are applicable and their limitations. Topics covered include search and heuristic search algorithms, Knowledge-representation (logic-based and probabilistic-based) inference and learning algorithms.

    Academic Honesty:

    Academic honesty is taken seriously. It is the responsibility of each student to be familiar with UCI's current academic honesty policies. Please take the time to read the current UCI Senate Academic Honesty Policies.


  • There will be 4-6 quizzes, approximately every 1-2 weeks on the material covered in class up to that time.
  • The quizzes will account for 15-20% of your overall grade. The lowest scored quiz will be dropped.
  • There will be one midterm exam, closed books which will account for 25% of the grade.
  • There will be 1 project and/or homeworks which will account for 15-20% of the grade.
  • There will be a final exam, closed books during the final week which will account for 40% of the grade.

  • Bulletin Board:

    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.


    Some handouts will be distributed during the quarter by the Distribution Center, others will be available to buy in the Engineering Copy Center.


    • Lecture 1. Introduction and overview: Goals, history, intelligent agents. Ch. 1, 2.
    • Lecture 2. Problem solving: State-spaces, search graphs, problem spaces, problem types. Ch. 3 .
    • Lecture 3-4. Uninformed search: greedy search, breadth-first, depth-first, iterative deepening, bidirectional search. Ch. 3.
    • Lecture 5. Informed Heuristic search: Best-First, A*, Properties of A*. Ch. 4.
    • Lecture 6. Informed Heuristic search: Branch and bound, IDA*, Generating heuristics automatically. Ch. 4.
    • Lecture 7. Game playing: Minimax search, Alpha-Beta pruning. Ch. 5.
    • Lecture 8. Constraint networks: The Constraint Satisfaction problem formulation, constraint-graphs, Consistency algorithms. Class-notes.
    • Lecture 9. Search in CSPs: forward-checking, solving trees, iterative improvement (hill climbing, stochastic local search.) Ch. 4.4.
    • Lecture 10. Representation and Reasoning: Propositional logic, inference, resolution, satisfiability. Ch. 6.
    • Lecture 11. Predicate logic: Syntax, Quantifiers, variables. Ch. 7.
    • Lecture 12. Inference in logic: Forward and backward inference, unification. Ch. 9.
    • Lecture 13. Midterm:
    • Lecture 14. Inference in logic (continued)
    • Lecture 15. Applications to planning, Ch.11:
    • Lecture 16. Agent reasoning under uncertainty, Ch. 15
    • Lecture 17. Learning from observations: Learning decision trees. Ch. 18.
    • Lecture 18. Perceptron model and neural networks, Ch. 19,
    • Lectures 19-20. Additional topics (reasoning with uncertainty, natural language, vision) as time permits

      Resources on the Internet

      A list of Web resources about AI .