CPSC 322: Introduction to Artificial Intelligence

CPSC 322 - Introduction to Artificial Intelligence (2017-18 WT1)

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.

Meeting Times: Tuesdays, Thursdays, 5 - 6:20 PM
First Class: Thursdays, September 7, 2017
Location: MCML 166
Instructor: Cristina Conati (conati@cs.ubc.ca)
- Office: ICICS/CS 107
- Office Hourse: Thursdays, 2-3pm (may still change)
TAs
- Borna Ghotbi (bghotbi@cs.ubc.ca)
- Vanessa Putnam (vputnam@cs.ubc.ca)
- Michael Przystupa (bot267@cs.ubc.ca)
- Wenyi Wang (wenyw@cs.ubc.ca)
TAs' Office Hours (all in Demco Table 4)

Day	Time	TA
Mondays	10:00-11:00am	Borna
Tuesdays	12:00-1:00pm	Michael
Wednesdays	1:30-2:30pm	Vanessa
Fridays	1:00-2:00pm	Wenyi

Course Discussion Board:
- We will use Piazza as a discussion forum for this course. Use this forum to ask any question you might have on course content, materials and assignments. We will not be answering these questions via email.
  - Sign up at: piazza.com/ubc.ca/winterterm12017/cpsc322/
If you have any problems or feedback for the developers, email team@piazza.com.
- Assume a turnaround time of 24 hours for getting answers from the teaching team (weekdays). The teaching team will monitor the discussion board during weekends prior to relevant deadline (exams, assignment due dates)
AISpace: demo applets that illustrate some of the techniques covered in class
Mideterm: TBA
Final exam: TBA

Grades

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.

Assignments -- 20%
Midterm -- 30%
Final -- 50%
Bonus Points for Clickers - 4%: 2% for participation, 2% for correctness

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.

Assignments -- 20%
Midterm -- 15%
Final -- 65%

Assignments will not necessarily be graded out of the same number of points; this means that they will not necessarily be weighted equally.

Submitting assignments via Connect: For each assignment an entry is created in Connect. You will use this entry for electronically submitting your assignments. Instructions on the files to be submitted will be provided for each assignment.

Late Assignments:

Assignments are to be handed via Connect by the specified deadline.
Every student is allotted two "late days", which allow assignments to be handed in late without penalty on two 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 time of the day an assignment is due. For instance, suppose an assignment is due at 1pm of a give day

Handing in an assignment on that day but one hour later (2pm) consumes one late day.
Handing in an assignment at 10:15 the morning after it is due consumes one late day.
Handing in an assignment at 1:30pm the day after the assignment is due consumes two late days.

To use a late day, write the number of late days claimed on the first page of your assignment.

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.

If an assignment cannot be completed, the assignment grade will be computed based on the remaining assignments. Note that such an arrangement is extremely unusual--the late day system is intended to allow students to accommodate disruptions from moderate illness without contacting the instructor.
If the midterm is missed, its grades will be shifted to the final. This means the final will count for 80% of the final grade, and assignments will count for the remaining 20%.
If the final is missed, you will need to take a make-up final (most likely at the next offering of the class).

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:

For most assignments, you may work with one other student (exceptions will be noted in the assignment instructions, as needed). That student must also be a CPSC 322 student this term, and you will both have to officially declare that you collaborated when submitting your assignment. Further instructions on how to submit an assignment in collaboration with another student will be provided in the assignment's cover sheet instructions.
You cannot work with or copy work from anyone else. You may not, under any circumstances, submit any solution not written by yourself, look at a student's solution who is not your official partner (this includes the solutions from assignments completed in the past), or previous sample solutions, and you may not share your own work with others.
All work for this course is required to be new work and cannot be submitted as part of an assignment in another course without the approval of all instructors involved.
Use of another person’s clicker or having someone use your clicker is considered plagiarism with the same policies applying as would be the case for turning in illicit written work.

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!

Text

Artificial Intelligence: Foundations of Computational Agents by Poole and Mackworth. (available in electronic form and at UBC Bookstore

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 (third edition). There will be a copy on reserve in the CS reading room.

Schedule

Below you can find the course schedule, lecture slides and other relevant material. The schedule is tentative and will change throughout the term. Future assignments due dates are provided to give you a rough sense; however, they are also subject to change. PLEASE CHECK THIS SCHEDULE OFTEN.

Date	Lecture	Book Chp	Notes
(1) Sept 7 (2) Sept 12	What is AI? [pdf]	1.1-1.3	Assignment 0 out (see Connect) Student on the waitlist can find it in piazza, post @10
(1) Sept 7 (2) Sept 12	Representational Dimensions, [pdf]	1.4-1.6
(3) Sept 14 (4) Sept 19	AI applications, Intro to Search [pdf]	3.1-3.4 3.5.1	Assignment 0 due Sept 14, 4:30pm Practice exercises 3A
(3) Sept 14 (4) Sept 19	Uninformed Search and Search with Costs [pdf]	, 3.5.2, 3.7.3, 3.5.3	Practice exercises 3B
(5) Sept 21 (6) Sept 26	Heuristic Search, BestFS, Admissible Heuristic, A* [pdf]	3.6 intro 3.6.1	Practice Exercises 3C, 3D Assignment 1 out Sept 21 (see Connect) ex-best.txt (AIspace example for Best-First Search) astar (AIspace example for A*)
(5) Sept 21 (6) Sept 26	Search Refinements Slides Prof. Carenini [pdf]	3.7.1-3.7.4,	AISpace example for pruning with A*
(7) Sept 28 (8) Oct 3	Search 7: Search WrapUp CSP Intro [pdf]	3.7.6 4.1, 4.2	Practice Exercise 3E
(7) Sept 28 (8) Oct 3	CSP as Search, [pdf]	4.3, 4.4,	simpleCSP AISpace example for Arc Consistency Assignment 1 due Oct 3, 11:59pm
(9) Oct 5 (10) Oct 10	Arc Consistency [[pdf]	4.5, 4.6	Assignment 2 out by Friday Oct 6 Practice Exercises 4a and 4b
(9) Oct 5 (10) Oct 10	Stochastic Local Search [pdf]	4.8.1, 4.8.2 - 4.8.3	Practice Exercise 4c
(11) Oct 12 (12) Oct 17	SLS Wrap up [pdf]
(11) Oct 12 (12) Oct 17	Planning Intro and Forward Planning [pdf]	8.1 - (except 8.1.2) ,8.2	Summary of Planning Competition 2008 (see slides 15-18 for participating planners, and slide 24 for domains) Practice Exercise 8.a
(13) Oct 19	Planning as CSP [pdf]	8.4	Practice Exercise 8.b, Practice Exercise 8.c, Assignment 2 due Thursday Oct 19, 11:59pm
Tuesday Oct 24	MIDTERM		Closed book. No calculators or e-devices allowed. Covers material up to forward planning included (but heuristics for forward planning are excluded)
(14) Oct 26 (15) Oct 31	Planning Wrap Up [pdf]	5.1 - 5.1.1, 5.1.2 - 5.2 (p. 163-165) (p. 167-174)	Practice Exercise 5a
	Planning Wrap Up [pdf]	5.1 - 5.1.1, 5.1.2 - 5.2 (p. 163-165) (p. 167-174)
	Logic: Intro, PDCL [pdf]
(16) Nov 2 (17) Nov 7
	Bottom-up and Top Down Proof Procedures [pdf] Datalog, Logic: wrap-up. Intro to Reasoning Under Uncertainty [pdf]	5.2.2 12 (only basic concepts covered in slides) 6.1, 6.1.1, 6.1.2	Assignment 3 out Thursday Nov 2, due Wednesday Nov 15, 11:59pm (LATE DAYS ALLOWED) kb-for-top-down-search, in-part-of,
		6.1.3
(18) Nov 9 (19) Nov 14	Uncertainty: Conditioning [pdf]
(18) Nov 9 (19) Nov 14	Uncertainty: Independence [pdf]	6.2	Assignment 3 due Wednesday Nov 15, 11:59pm - Two late days allowed
(20) Nov 16 (21) Nov 21	Belief Nets: Construction, CPTs [pdf]	6.3.1	Practice Exercise 6.a (directed questions) Practice Exercise 6.a (part 4)
(20) Nov 16 (21) Nov 21	Belief Nets: Structure [ppt]		Practice Exercise 6.b Practice Exercise 6.c, Assignment 4 out Wednesday Nov 22, due Dec 1, 11:59pm (LATE DAYS ALLOWED)
(22) Nov 23 (23) Nov 28	Belief Nets: Variable Elimination [pdf]	6.4.1 9.2
(22) Nov 23 (23) Nov 28	Planning under Uncertainty and Decision Networks [pdf]	9.2	Practice Exercise 9a Practice Exercise 9b
(24) Nov 30	VE for Decision Networks [pdf]	9.3
(24) Nov 30			Assignment 4 due Friday Dec 1, 11:59pm