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CPSC 433: Artificial Intelligence - Materials


Textbooks

The following is a collection of text books on AI. I recommend that you look at them in the library (and any other AI books you find there) and decide for yourself which one you find best (i.e. which one explains the best the things that you did not understand in the lectures and labs). They all have a rather large overlap in their content and none of them covers all of the course (in the depth that I want the different topics covered). Note that some of them are out of print (but you might be able to buy used copies cheap).

  • Introduction to Artificial Intelligence - Charniak, McDermott (Addison Wesley), 1985.
  • Artificial Intelligence - Second Edition - Rich, Knight (McGraw Hill), 1991.
  • Artificial Intelligence - Luger (Addison Wesley), 1997.

Files to the course

This file contains two chapters of a book I have written in German and I am in the process of translating it into English (I am in the process for 15 years now, so that it is questionable that I will ever publish it, but there is still a little hope). It explains compactly search in general and the search paradigms we will be covering in this course. Note that since the book is not published yet, I have restricted access to computers in the ucalgary domain.

I have also put together a list of questions aimed at preparing you for the Midterm and the Final. Note that the Final will also cover all of the topics of the Midterm! Students also frequently ask for some search problems they can use to prepare for exams (since there are a lot of questions in the list that ask for formalizing some parts of a search problem).

The slides containing the general information on a particular topic that I use in the lectures will be available before the respective lectures here as Acrobat pdf-files (in two formats: one slide per page and the 6 slides per page handout). It is recommended that students take a look at the slides before I go over them in lecture so that they can ask questions and are prepared for the examples we will be going through (they are not completely part of the slides).

Date handout format one slide per page
July 25 Introduction Introduction
Sep. 12 Knowledge Processing Intro Knowledge Process ing Intro
Sep. 17 Search: Basic Definitions Search: Basic Definitions
Sep. 21 Set-based search Set-based search
Sep. 28 And-tree-based search And-tree-based search
Oct. 9 Or-tree-based search Or-tree-based search
Oct. 16 Other search models Other search models
Oct. 17 Search controls Search controls
Oct. 17 Knowledge representation: logic Knowledge representation: logic
Oct. 24 Knowledge representation: logic Knowledge representation: logic
Nov. 1 Knowledge representation: rules Knowledge representation: rules
Nov. 12 Knowledge representation: frames Knowledge representation: frames
Nov. 17 Knowledge representation: semantic networks Knowledge representation: semantic networks
Nov. 21 Knowledge representation: neural networks Knowledge representation: neural networks
Nov. 29 Knowledge representation: constraints Knowledge representation: constraints
Dec. 4 Learning: motivation Learning: motivation

to the assignments of the course.

Last Change: 4/12/2017