The 1st, 2nd, 3rd, 4th and 5th handouts are related to the 1st
assignment.
 How to use Eclipse Prolog 6.0 in
labs.

Getting
started with ECLiPSe Prolog. This is User Manual for the recent
release 6.0 of ECLiPSe Prolog. Note that all documentation is available
online. An introductory manual for an older version (for Prolog 5.8) is
available as
this PDF file.
 An example of Prolog program: includes an exercise (with
answers).
 A blocks world (blocks.pl)
 Fun with recursion : the file reach.pl
(try queries for different cities if they are reachable or not).
The following 2 handouts and the previous 2 handouts are somewhat
related to the 2nd assignment.
 Recursion over lists (replace.pl)
Structures: terms in Prolog (a binary tree
example).
These 2 handouts are somewhat related to the 3rd assignment.
 Make a choice of a lunch (the file lunch.pro)

The problem of visual scene interpretation.
Here is the
formulation of the problem (the file vision.pdf).
The left map and the right map
represent maps mentioned in the formulation. Here is the incomplete solution
(the file part3.pl) that is mentioned in the handout.
Your task is to complete this program and make sure it works with both
maps given to you.
This handout is related to the 5th assignment.

A handout on precondition and successor state axioms for the
blocks world.

A handout on
robotics
(PostScript file) and a
PDF file. This handout is not mandatory:
you may skip it if you have no time to read.
All handout below are related to Bayesian Networks.
 In class, I used slides prepared by
Finn Jensen and Thomas Nielsen for their book
Bayesian Networks and Decision Graphs, 2nd edition, published
by Springer in 2007. In particular, I used slides to
Chapter 1
that provide a review of basic probability theory: slides 115
on the pages 110 only (the last slide is "Marginalization").
 Pages 2327 provide
Motivation for Bayesian Networks: This PDF file includes slides to
Chapter 13 of the textbook "Artificial Intelligence: A Modern Approach"
by Stuart Russell and Peter Norvig.
Causal networks:
serial, diverging and converging connections and examples when evidence
from instantiation of one node can pass to other nodes in the networks.
Introduction to Bayesian Networks
(skip slides 3847 on pages 2427)
with an example of computing joint probability by relying on the
structure of the network. Excerpts are taken from slides to Chapter 2
(in class, we skipped slides 2334) and are prepared by Jensen
and Nielsen for their textbook
"Bayesian Networks and Decision Graphs" mentioned above.
 Last class: Earthquake example. I used
slides to Chapter 14 (slides 111 only) from the textbook
"Artificial Intelligence: A Modern Approach"
by Stuart Russell and Peter Norvig. Here is a
handout with 6 slides per page.

The course CS 221: "Artificial Intelligence: Principles & Techniques"
(Stanford University, California, USA) includes Bayesian Networks.
You can read 2011 Lecture on
Probability and Bayes Nets (PDF) and the
Slides prepared for
Stanford CS221: Introduction to Artificial Intelligence,
a course taught in 2011 by Professors Sebastian Thrun and Peter Norvig.

Norman Fenton's tutorial on
Probability Theory and Bayesian Networks
includes:

section on
Probability Theory Fundamentals
(variables and probability distributions,
joint events and marginalisation, conditional probability,
Bayes rule, chain rule, independence and conditional independence)

and a detailed account of
Bayesian belief networks
(definitions, how to use them, why we should use them).

Irad BenGal
Bayesian Networks: you can read pages 13 only (Introduction and Inference).
Accessible only from computers within Ryerson network.
This article was published in
Encyclopedia of Statistics in Quality and Reliability,
Copyright © 2007 John Wiley & Sons, Ltd.
DOI: 10.1002/9780470061572.eqr089.
Article Online Posting Date: March 15, 2008

Kevin B. Korb and Ann E. Nicholson
Bayesian Artificial Intelligence: this Webpage includes
research and various resources on Bayesian Networks.
Visiting this Web page is not mandatory.