University of Toronto - Winter 2017
Department of Computer Science

CSC 384: Introduction to Artificial Intelligence

Lecture Slides and Readings


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The course material will be covered primarily in lectures and tutorials. Some examples will be done in class only, and will not appear in these notes. It is your responsibility to take notes in class to augment  these slides with the extra pertinent information presented during class.

The recommended text book also contains material that will help clarify the topics covered in the lectures.


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Topic Readings
Russell and Norvig (R&N)
Class
Slides
Notes
Introduction
What is AI
Chapter 1 presents a more complete and very interesting overview of the history and goals of AI research.

Chapter 2 also contains some interesting ideas about one way to think about the structure of AI systems.

01-Introduction
01-Introduction (4 pp)

Uninformed and Heuristic Search Chapter 3 presents the search techniques covered in the lectures.

Chapter 4 can be read for enrichment at this point. We'll return to some ideas in this chapter later in the course.

02-Uninformed Search
02-Uninformed Search (4pp)

02-Heuristic Search
02-Heuristic Search (4pp)

02-Heuristic Search Tutorial
02-Heuristic Search Tutorial(4pp)

[Sheila:] These are interim slides and may be updated slightly.

Why Watson incorrectly responded "What is Toronto?" (from a friend on the Watson team)

Ariel Felner's discussion of Dijkstra's Algorithm vs UCS

Properties of uninformed search from RN 3rd edition (with their algorithms).

Videos: Great pathfinding simulations by Nathan Sturtevant, Rick Valenzano and others

fun IDA* GIF
Constraint Satisfaction Problems Chapter 5.1-5.2 (R&N, 2nd ed)

Chapter 6.1, 6.2, 6.3 (R&N, 3rd ed.)
04-Backtracking Search
04-Backtracking Search (4pp)

CSP Class Exercise
CSP GAC Class Exercise Soln
[Sheila:] These are interim slides and may be updated slightly.
Game Tree Search Chapter 6.1,6.2,6.3 (R&N,2nd ed)

Chapter 5.1, 5.2, 5.3 (R&N,3rd ed)

Chapter 6.6 (respectively 5.7) also makes for interesting reading!
03-GameTreeSearch

03-GameTreeSearch (4pp)

Make sure to go through the alpha-beta pruning walk-thru in the notes section.


The following is a fun program for practicing alpha-beta pruning Sheila: Sorry it doesn't seem to be working!

Another fun alpha-beta practice program

The following is a great walk-thru of alpha-beta pruning.



[Sheila:] These are interim slides and may be updated slightly.

Probability Review,
Intro to Bayesian Networks
Chapters 13 and 14 (R&N, 2nd or 3rd ed) 05-Uncertainty (Part 1)

05-Uncertainty (Part 1) (4pp)

The full lecture notes:
05-Uncertainty (Full)

Uncertainty Tutorial

***Please see Errata sheet for correction to slide***

[Sheila:] These are interim slides and may be updated slightly.

Midterm Review
Midterm Review
Midterm Review (4pp)


Special Lecture on Monte Carlo Tree Search (MCTS)
and
the Fixed Project

MCTS Tutorial
** Not on the exam **

Good Tutorial on MCTS

Good Video on MCTS
Knowledge Representation and Reasoning

Chapter 7-10 (R&N, 2nd ed)

Chapter 7-9, 12 (R&N, 3rd ed.)

05-KR

05-KR (4pp)

***Please see Errata sheet for correction to slide***

[Sheila:] These are interim slides and may be updated slightly.

KR Tutorial Slides.
Planning Chapter 10.3 and 11 (R&N, 2nd ed)
Chapter 10 (R&N, 3rd ed.)
Abridged 06-Planning
** Not on the exam **

The posted slides include what was covered in class, which is an abridged version of all the material on planning.

Check out planning.domains: a collection of tools for working with planning domains and problems.
Final Review
Final Review
Final Review (4pp)

A resolution example and a few more slides on KR:
Resolution Example(**)

(**) I missed corrrecting the very last slide of the resolution proof. "markus" should be "marcus" and line 15 should be [14a,4] as in previous slides.


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