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Overview
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: Monday, Wednesday, Friday, 1:00
- 1:50 pm
- First Class: Wednesday, September 7, 2011
- Location: DMP 301
- Instructor: Alan K. Mackworth
- Instructor's Office Location: ICCS 121
- Instructor's Office Hours: Monday,
Wednesday, Friday 2:00 - 2:30 pm
- TAs: (TA Office Hours are in the Demco
Learning Centre, ICCS 150)
- Course Discussion Board: (the place to submit
your questions and get answers, as well as see answers
given to others): log into WebCT
Vista using your CWL
- AISpace:
demo applets that illustrate most of the techniques
covered in class
- Prerequisites: Either (a) CPSC
221 or (b) both of CPSC
216, CPSC
220 or (c) all of CPSC
211, CPSC
260, EECE
320.
- Final exam: Tuesday, December 20, 2011 3:30 -
6:00 pm
What do I do if I get the flu?
- Self-isolate: stay away from campus
until you're fever-free for 24 hours.
- Get a doctor's note if you're missing midterm
or final, or if you'll be late for an assignment.
- Follow the course on this page, and
contact Alan if you have additional questions.
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%
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%
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 account
activation page.
Late Assignments: Assignments
are to be handed in electronically via Handin
by 1 pm 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
1:00 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:
- Handing in an assignment at the end of lecture on the
day it is due consumes one late day.
- Handing in an assignment at 10:15 am the morning after
it is due consumes one late day.
- Handing in an assignment at 1:30 pm the day after it
is due consumes two late days.
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, a make-up final will be
scheduled. This make-up final will be held as soon as
possible after the regularly scheduled final.
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 assignments 1-4 (not for assignment 0), you
may work with one
other student. 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. Both of you will have to submit your
assignments separately.
- 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.
- You may, however, discuss your solutions
and design decisions with your fellow students on a
high level. In other words, you can talk about the
assignments, but you cannot look at or copy
other people's answers.
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
We will be using the text Artificial Intelligence:
Foundations of Computational Agents by Poole
and Mackworth. The entire book is available in e-format at
the above link. Copies are available in the UBC Bookstore
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 (Third
edition). I've arranged for a copy to be put
on reserve in the CS
reading
room.
Schedule
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 11 pm the day before 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.
| Date |
Lecture |
Book Sections
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Notes
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| (1)
Wed, Sep 7 |
Intro
1:
What
is
AI?
(.ppt) (.pdf) |
1.1-1.3
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Assignment
0 out |
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| (2) Fri, Sep 9 |
Intro 2:
Representational Dimensions (.ppt) (.pdf) |
1.4-1.5 |
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| (3) Mon, Sep 12 |
Intro
3: Applications of AI (.ppt) (.pdf) |
1.6
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| (4) Wed, Sep 14 |
Search
1:
Representation & Search Framework (.ppt) (.pdf) |
3.0-3.4
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Assignment 0
due |
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| (5) Fri, Sep 16 |
Search 2:
BFS and DFS
(.ppt) (.pdf) |
3.5 |
Exercise
1, Solutions
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| (6) Mon, Sep 19 |
Search
3:
Search
with
Costs & Heuristic Search
(.ppt) (.pdf) |
3.5.3, 3.6.1
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| (7) Wed, Sep 21 |
Search
4:
Heuristic
Search:
A*
(.ppt) (.pdf) |
3.6 |
Exercise
2, Solutions |
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| (8) Fri, Sep 23 |
Search 5: A*
optimality, cycle checking (.ppt) (.pdf) |
3.6
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| (9) Mon, Sep 26 |
Search
6:
Iterative
Deepening
(IDS)
(.ppt) (.pdf) |
3.7.3 |
Assignment
1 out
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| (10) Wed, Sep 28 |
Search
7:
Multiple
Path Pruning, IDS
(.ppt) (.pdf) |
3.7.1-3.7.3 |
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| (11) Fri, Sep 30 |
CSP
1: Branch
&
Bound.
CSP:
Intro
(.ppt) (.pdf)
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3.7 & 4.0-4.2 |
Exercise 3, Solutions
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| (12) Mon, Oct 3 |
CSP
2: Solving
CSP
using
search
(.ppt) (.pdf)
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4.3-4.4 |
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| (13)
Wed, Oct 5 |
CSP 3:
Arc consistency (.ppt)
(.pdf) |
4.5
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Exercise 4, Solutions
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| (14) Fri, Oct 7 |
CSP 4:
Domain splitting (.ppt) (.pdf) |
4.6
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Assignment
1
due
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| Mon, Oct 10 |
Thanksgiving |
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(15)
Wed, Oct 12
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CSP 5:
Local search(.ppt)(.pdf)
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4.8
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Assignment 2
out
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| (16) Fri,
Oct 14 |
CSP 6:
Stochastic local search(.ppt)(.pdf) |
4.8 |
Exercise
5, Solutions
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(17) Mon,
Oct 17
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CSP 7:
Stochastic local search algorithms (.ppt) (.pdf)
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4.8 |
roundabouts.xml |
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| (18) Wed,
Oct 19 |
Planning
1:
Representation
and
Forward
Planning
(.ppt)(.pdf) |
8.0, 8.1, 8,2 |
Exercise 6, Solutions |
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| (19) Fri, Oct 21 |
Planning
2: Forward
Planning
and
CSP
Planning (.ppt)(.pdf) |
8.2, 8.4
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Assignment
2
due
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| Mon, Oct 24 |
Midterm review |
|
Exercise
7,
Solutions |
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| Wed, Oct 26 |
Midterm |
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| (20) Fri, Oct 28 |
Planning
3:
CSP
Planning
wrap
up.
(.ppt) (.pdf) |
8.4 |
Assignment
3 out
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| (21) Mon, Oct 31 |
Logic
1:
Intro
&
Propositional Definite Clause Logic
(.ppt)
(.pdf)
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5.1-5.2
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Exercise
8,
Solutions |
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| (22) Wed, Nov 2 |
Logic
2:
Proof
procedures,
soundness and completeness (.ppt)
(.pdf)
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5.2 |
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| (23) Fri, Nov 4 |
Logic
3:
Bottom-up
and
Top-down Proof Procedures (.ppt)
(.pdf)
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5.2 |
Exercise 9,
Solutions
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| (24) Mon, Nov 7 |
Logic
4:
Top-Down Procedure,
Datalog and Big Picture (.ppt)
(.pdf)
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5.2, 12.3
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| (25) Wed, Nov 9 |
Uncertainty 1:
Probability Theory: Intro (.ppt)
(.pdf)
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6.1, 6.1.1,6.1.3 |
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| Fri, Nov 11 |
Remembrance
Day: no class
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| (26) Mon, Nov 14 |
Uncertainty
2:
Conditional
Probability,
Bayes Rule, Chain Rule(.ppt)(.pdf) |
6.1.3 |
Assignment
3 due |
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| (27) Wed, Nov 16 |
Uncertainty
3: Independence
(.ppt)(.pdf) |
6.2 |
Assignment 4
out |
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| (28) Fri , Nov 18 |
Uncertainty 4:
Bayesian networks intro (.ppt)
(.pdf)
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6.3 - 6.3.1 |
Exercise 10
Solutions
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| (29) Mon, Nov 21 |
Uncertainty 5:
Independence and Inference (.ppt)
(.pdf)
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6.3.1 |
credit_card_fraud.xml |
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| (30) Wed,
Nov 23 |
Uncertainty
6: Variable Elimination (.ppt)
(.pdf)
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6.4.1 |
Exercise 11 Solutions |
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(31) Fri, Nov 25
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Decision
Theory 1: Uncertainty wrap-up. Single
Decisions (.ppt)
(.pdf)
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6.4.1 & 9.2 |
newspaper.xml |
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(32) Mon, Nov 28
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Decision
Theory
2:
Single
and
sequential
decisions.
VE.
(.ppt)
(.pdf)
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9.2-9.3 |
Exercise
12 Solutions
bikeride_tires_flat_tools.xml
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| (33) Wed , Nov 30 |
Decision
Theory
3:
optimal
policies for sequential decisions (.ppt)
(.pdf)
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9.3 |
Assignment
4 due
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| (34) Fri, Dec 2 |
Perspectives
and Final Review (.ppt)
(.pdf)
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Exercise
13 Solutions
wii.xml
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Tue, Dec 20, 3:30-6:00
pm
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Final exam
in DMP 310
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Assessing
Your
Own Learning
- We have created a list of learning goals for
the course, which detail concrete skills
you should have after mastering each of the units. The
list is available via WebCT
Vista.
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Exercises are ungraded
practice problems to help you prepare for
assignments and exams. They're optional, but will
definitely help you to master the course material.
All the exercises will be put up here, with
solutions, on the schedule.
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