CPSC 322 - Introduction to Artificial Intelligence
(Term 2, Session 201, 2010-11)

Overview Grades Text Schedule Assessing your own learning



Course Description:  This course provides an introduction to the field of artificial intelligence.  The major topics covered will include reasoning and representation, search, constraint satisfaction problems, planning, logic, reasoning under uncertainty, and planning under uncertainty.

What do I do if I get the flu?


Grading Scheme: Evaluation will be based on a set of  assignments, a midterm, and an exam. Important: you must pass the final in order to pass the course. The instructor reserves the right to adjust this grading scheme during the term, if necessary.

If your grade improves substantially from the midterm to the final, defined as a final exam grade that is at least 20% higher than the midterm grade, then the following grade breakdown will be used instead.

The assignment grade will be computed by adding up the number of points you get across all assignments, dividing this number by the number of possible points, and multiplying by 20.  Assignments will not be graded out of the same number of points; this means that they will not be weighted equally.


Submitting assignments via handin: Assignments are to be handed in electronically via the handin tool. The WebCT discussion board for assignment 0 has instructions for how to do this. In order to use handin, you will need to activate your CS account (every registered student already has an account, you just need to activate it). You can activate your account on the acount activation page.

Late Assignments: Assignments are to be handed in electronically via Handin by 3pm on the due date (see the point above). However, every student is allotted four "late days", which allow assignments to be handed in late without penalty on four days or parts of days during the term.  The purpose of late days is to allow students the flexibility to manage unexpected obstacles to coursework that arise during the course of the term, such as travel, moderate illness, conflicts with other courses, extracurricular obligations, job interviews, etc.  Thus, additional late days will NOT be granted except under truly exceptional circumstances.  If an assignment is submitted late and a student has used up all of her/his late days, 20% will be deducted for every day the assignment is late. (E.g., an assignment 2 days late and graded out of 100 points will be awarded a maximum of 60 points.)

How late does something have to be to use up a late day? A day is defined as a 24-hour block of time beginning at 3 PM on the day an assignment is due.  To use a late day, write the number of late days claimed on the first page of your assignment.. Examples:

Missing Deadlines or Exams: In truly exceptional circumstances, when accompanied by a note from Student Health Services or a Department Advisor, the following arrangements will be made.

Academic Conduct: Submitting the work of another person as your own (i.e. plagiarism) constitutes academic misconduct, as does communication with others (either as donor or recipient) in ways other than those permitted for homework and exams. Such actions will not be tolerated. Specifically, for this course, the rules are as follows:

Violations of these rules constitute very serious academic misconduct, and they are subject to penalties ranging from a grade of zero on the current and *all* the previous assignments to indefinite suspension from the University. More information on procedures and penalties can be found in the Department's Policy on Plagiarism and collaboration and in  UBC regulations on student discipline . If you are in any doubt about the interpretation of any of these rules, consult the instructor or a TA!


We will be using the text Artificial Intelligence: Foundations of Computational Agents by Poole and Mackworth. The entire book is available in electronic form via the above link, and a copy is on reserve in the CS reading room.. Although this text will be our main reference for the class, it must be stressed that you will need to know all the material covered in class, whether or not it is included in the readings or available on-line.  Likewise, you are responsible for all the material in assigned readings, whether or not it is covered in class. If you'd like to refer to an alternate text, I recommend Russell and Norvig's Artificial Intelligence: A Modern Approach (second edition). I've arranged for a copy to be put on reserve in the CS reading room.


You can find the course schedule and lecture slides below. The schedule is tentative and will change throughout the term. Future assignment due dates are provided to give you a rough sense; however, they are also subject to change. I will try to post the slides for each lecture by 2am the day of the lecture; this allows you to print them when you get up in the morning. I don't promise to use exactly that version in class, but it should be very close. If I do further changes, I will post the final version after class, at the latest when I post the slides for the next lecture.
If you are curious what's coming up in future lectures, here are Cristina Conati's slides from last term; the content should be quite similar to this year but the form differs a lot (so these are NOT useful for note taking in class).


Date Lecture Book Sections
(1) Wed, Jan 5 Intro 1: What is AI? [ppt] [pdf] 1.1-1.3
Assignment 0 out
(2) Fri, Jan 7 Intro 2: Representational Dimensions [ppt] [pdf] 1.4-1.5
(3) Mon, Jan 10 Intro 3: Applications of AI [ppt] [pdf] 1.6

(4) Wed, Jan 12 Search 1: Representation & Search Framework [ppt] [pdf] 3.0-3.4
Assignment 0 due
(5) Fri, Jan 14 Search 2: BFS and DFS [ppt] [pdf]  3.5 Exercise 1 , solutions
(6) Mon, Jan 17 Search 3: Search with Costs & Heuristic Search [ppt] [pdf]  3.5.3, 3.6.1

(7) Wed, Jan 19 Search 4: Heuristic Search: A* [ppt] [pdf] 3.6  Exercise 2 , solutions
(8) Fri, Jan 21 Search 5: A* optimality, cycle checking [ppt] [pdf] 3.6

(9) Mon, Jan 24 Search 6: Iterative Deepening (IDS) [draft:ppt pdf] [covered: ppt pdf] 3.7.3  Assignment 1 out
(10) Wed, Jan 26 Search 7: Multiple Path Pruning, IDS [draft:ppt pdf] [covered: ppt pdf] 3.7.1-3.7.3 
(11) Fri, Jan 28 CSP 1: Branch & Bound. CSP: Intro [ppt] [pdf] 3.7 & 4.0-4.2 Exercise 3 , solutions
(12) Mon, Jan 31 CSP 2: Solving CSP using search [ppt] [pdf] 4.3-4.4
(13) Wed, Feb 2 CSP 3: Arc consistency [ppt] [pdf] 4.5
Exercise 4 , solutions
(14) Fri, Feb 4 CSP 4: Domain splitting [draft: ppt pdf] [covered: ppt pdf] 4.6 Sudoku programming question out
(15) Mon, Feb 7
CSP 5: Local search [ppt] [pdf]
4.8 Exercise 5 , solutions
Assignment 1 due

Assignment 2 out
(16) Wed, Feb 9 CSP 6: Stochastic local search [ppt] [pdf] 4.8
(17) Fri, Feb 11
CSP 7: Stochastic local search algorithms [ppt] [pdf]
4.8  IBM's Watson in the news: NYTimes, wired magazine
More technical: AI magazine article: Building Watson
Watch: video of practice round
Mon, Feb 14 Reading break; university closed  
Wed, Feb 16 Reading break; university closed  
Fri, Feb 18 Reading break; university closed  
(18) Mon, Feb 21 Planning 1: Representation and Forward Planning [ppt] [pdf] 8.0, 8.1, 8,2  Exercise 6 , solutions
(19) Wed, Feb 23 Planning 2: Forward Planning and CSP Planning [ppt] [pdf] 8.2, 8.4
Assignment 2 due
Fri, Feb 25 Midterm review (all whiteboard, no slides)  
Mon, Feb 28 Midterm (3pm-4:30pm in FSC 1005)   Exercise 7 , solutions
(20) Wed, Mar 2 Planning 3: CSP Planning wrap up. [ppt] [pdf] 8.4  Assignment 3 out
(21) Fri, Mar 4 Logic 1: Intro & Propositional Definite Clause Logic [ppt] [pdf]  5.1-5.2
Exercise 8 , solutions
(22) Mon, Mar 7 Logic 2: Proof procedures, soundness and correctness [ppt] [pdf]  5.2 
(23) Wed, Mar 9 Logic 3: Bottom-up Proof Procedure [ppt] [pdf] 5.2  Exercise 9 , solutions
(24) Fri, Mar 11 Logic 4: Top-Down Procedure and Datalog [ppt] [pdf] 5.2 
(25) Mon, Mar 14 Logic 5: wrap-up [ppt] [pdf] & presentation: SLS for UBC exam scheduling 12.3 
(26) Wed, Mar 16

Uncertainty 1: Probability Theory [ppt] [pdf]

6.1, 6.1.1
Exercise 10 , solutions
Assignment 3 due
(27) Fri, Mar 18 Uncertainty 2: Conditional Probability, Bayes Rule, Chain Rule [ppt] [pdf] 6.1.3  Assignment 4 out
(28) Mon, Mar 21 Uncertainty 3: Independence [ppt] [pdf] 6.2
(29) Wed, Mar 23 Uncertainty 4: Bayesian networks intro [ppt] [pdf] 6.3 - 6.3.1
(30) Fri, Mar 25 Uncertainty 5: Independence and Inference [ppt] [pdf] 6.3.1
(31) Mon, Mar 28 Uncertainty 6: Variable Elimination [ppt] [pdf] 6.4.1 Exercise 11 , solutions
(32) Wed, Mar 30
Decision Theory 1: Uncertainty wrap-up. Single Decisions [ppt] [pdf] 6.4.1 & 9.2  newspaper.xml
(33) Fri, Apr 1 Decision Theory 2: Single and sequential decisions. VE. [ppt] [pdf] 9.2-9.3  Exercise 12 , solutions bikeride_tires_flat_tools.xml
(34) Mon, Apr 4 Decision Theory 3: optimal policies for sequential decisions [ppt] [pdf  9.3 Assignment 4 due
(35) Wed, Apr 6 Perspectives and Final Review [ppt] [pdf   Exercise 13 , solutions wii.xml
Mon, Apr 11 Final exam, 3:30 pm DMP 310. This is on the first day of exams.   

Assessing your own learning