CPSC 322 - Introduction to Artificial Intelligence (Term 2, Session 201, 2008-09)
Overview  Grades Text Schedule Handouts



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.


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.


Late Assignments: Assignments are to be handed in BEFORE the start of lecture on the due date. However, every student is allotted four "late days", which allow assignments to be handed in late without penalty on three 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 4 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 and submit your assignment to the course drop box located in the basement of CICSR, or just bring it to class if it's less than an hour late. Examples:

Assignments can be handed in electronically using handin; this is the only way to hand in late assignments over a weekend. Written work can also be put in Giuseppe's mailbox in the main CS office (room 201); ask the secretary to time-stamp it.


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 a new text under development, which is currently only available in electronic form: Artificial Intelligence: Foundations of Computational Agents by Poole and Mackworth. PDF files of the chapters covered in class will be added to WebCT as they are needed. 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.


Here is where you can find the course schedule and the PDF files from lectures. These dates will change throughout the term, but this schedule will be kept up to date.  Assignment due dates are provided to give you a rough sense; however, they are also subject to change. I will try to always post the slides in advance (by noon). After class, I will post the same slides inked with the notes I have added in class.


Date Lecture Notes
Mon, Jan 5 What is AI? [pdf] Assignment 0
Wed, Jan 7 Representational Dimensions [pdf]  
Fri, Jan 9 Applications of AI [pdf] Assignment 0 due
Mon, Jan 12 Search: Intro [pdf]  
Wed, Jan 14 Search: Uninformed Search, DFS and BFS [pdf] ex1 ex2 (AIspace)
Fri, Jan 16 Search: IDS, Search with Costs [pdf] ex3
Mon, Jan 19 Search: Heuristic Search [pdf]  
Wed, Jan 21 Search: BestFS, A*, optimality,  [pdf] ex4  Assignment 1 out (see WebCT)
Fri, Jan 23 Search: A* optimalEfficiency Branch&Bound.... [pdf]  
Mon, Jan 26 Search: Dynamic Prog. and Recap [pdf]  
Wed, Jan 28 CSP Introduction [pdf]  
Fri, Jan 30 CSPs: Search and Consistency [pdf]  
Mon, Feb 2 CSPs: Arc Consistency & Domain Splitting [pdf]  
Wed, Feb 4 CSPs: Local Search [pdf] Assignment 1 due
Fri, Feb 6 CSPs: Stochastic Local Search  [pdf] Assignment 2 out
Mon, Feb 9 CSPs: SLS variants (Sim. Annealing and Pop. based) [pdf]  
Wed, Feb 11 Planning: Representations and Forward Search [pdf]  
Fri, Feb 13 Planning: Heuristics and CSP Planning [pdf] ex5 delivery robot STRIPS->CSP
Mon, Feb 16 Midterm Break  
Wed, Feb 18 Midterm Break  
Fri, Feb 20 Midterm Break  
Mon, Feb 23 Logic: Intro and Syntax [pdf]  
Wed, Feb 25 Logic: Semantics and Bottom-Up Proofs [pdf] Assignment 2 due
Fri, Feb 27 Logic: BU Sound and Complete [pdf]  
Mon, Mar 2 Logic: Domain Modeling and Top-Down Proofs [pdf]  
Wed, Mar 4

Midterm exam (1.5 hours, regular room)

solution q.3 (load in AIspace)
Fri, Mar 6 Logic: Datalog [pdf] ex5.9  exDatalog (load in AIspace)
Mon, Mar 9 Uncertainty: Probability Theory [pdf]  
Wed, Mar 11 Uncertainty: Conditional Probability [pdf] Assignment3 out
Fri, Mar 13 Uncertainty: Conditional Independence [pdf]  
Mon, Mar 16 Uncertainty: Belief Networks [pdf] burglary example (load in AIspace)
Wed, Mar 18 Uncertainty: Belief Nets (indep. compactness, apps) [pdf] email spam ex. (load in AIspace)
Fri, Mar 20 Uncertainty: BNs inference ( intro Variable Elimination) [pdf]  
Mon, Mar 23 Uncertainty: Variable Elimination Example [pdf] Assignment3 due
Wed, Mar 25 Uncertainty: Temporal Probabilistic Models [pdf] Assignment4 out Blackjack.xml
Fri, Mar 27 Uncertainty: Hidden Markov Models [pdf]  
Mon, Mar 30 Decision Theory: Single-Stage Decisions [pdf] robot example (load in AIspace)
Wed, Apr 1 Decision Theory: Sequential Decisions (policies) [pdf] umbrella example (load in AIspace)
Fri, Apr 3 Decision Theory: VE , Value of Info and Control [pdf]  
Mon, Apr 6 Decision Theory: MDPs [pdf]  
Wed, Apr 8 Decision Theory: Finish MDPs [pdf] Assignment4 due
Fri Apr 24, 3:30 pm

Final Exam (3 hours, DMP 110)




Please note that the links to slides and to assignments are given in the schedule above.  The textbook chapters are only available through WebCT. Other handouts follow: