Professor Harry Wechsler

Department of Computer Science

George Mason University

Fairfax, VA 22030
 
 

e-mail : wechsler@cs.gmu.edu

www: http://cs.gmu.edu/~wechsler/personal

(703)993-1533 (office)

(703)993-1530 (sec)

(703)993-1710 (fax)
 
 

GEORGE MASON UNIVERSITY

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SPRING  2002

 

Midterm Exam Date      :  Wednesday,  March 20  :   7:30 p.m.  – 10:00 p.m.

Final  Exam Date           :  Wednesday,  May 8       :    7:30 p.m. – 10:15 p.m.

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CS 580 - Artificial Intelligence (AI)
 
 

Class Information

001  30734    W     7:20 p.m. – 10.00 p.m.   R    B105

Office Hours

W    6 – 7 p.m. or by appointment (SITE II - Rm. 461)

Textbook

Artificial Intelligence  (4th. Edition) by  George Luger, Addison Wesley, 2002

TA

Elena Popovici epopovic@cs.gmu.edu

T   4 – 7 PM (SITE II – rm. 365)

Programming Languages

Here are some links for free LISP's, PROLOG's :

1. Free LISP integrated development environments for Windows or Linux:

Harlequin LispWorks Personal Edition
http://services.harlequin.com/lisp/lww.nsf/RegistrationPersonal?OpenForm

2. Free PROLOG environments for Windows or Linux:

SWI-Prolog
http://swi.psy.uva.nl/projects/SWI-Prolog/download.html
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MATLAB primer available at :

http://www.cs.gmu.edu/~zduric/cs580/primer40.ps

access to MATLAB from both CS and IT&E

for further information use 'help' and 'demo'

Course Description

The course is about automation of intelligent behavior. Covers principles
and methods for predicate calculus and automatic reasoning,
intelligent (heuristic search), game playing and problem solving,  knowledge representation,
raesoning with uncertainty and belief (Bayes) networks, (symbolic, connectionist and evolutionary)
learning, natural language processing,  and Human-Computer Intelligent Interaction (HCI). 
LISP, PROLOG, and MATLAB are  the programming languages of choice used  to implement
the AI methods learned during the course. Approach used throughout the course is to address
specific intelligence tasks, motivate how to solve them, describe algorithmic solutions, and
consider comparative performance evaluation.

Grading

Late Homework is not accepted.

Each homework consists of two parts: programming project
and exercises from the textbook. Submit the programming part electronically
to the TA.  Submit the exercises using hard copy during the lecture time.
The grade will be marked on the hardcopy returned to you.

Homework #1,  #2 and #3   à 60 %

MIDTERM à March 20 à  10 % à covers January 23 – February 27 lectures

FINAL à May 8  à 30 % à covers everything

Schedule
 
 

January 23

Chs. 1 : History and Applications. Reading assignment : Is the Brain a Digital Computer by John R. Searl from http://cogsci.soton.ac.uk/~harnad/Papers/Py104/searle.comp.html

 

January 30 – February 13

Ch. 2 :  Predicate Calculus; Ch. 12.0 – 12.2 : GPS and Resolution Theorem Proving – individual study : Ch. 14 : PROLOG

February  20

Ch. 3 : Structures and Strategies for State Space Search – Ch. 4 : Heuristic Search;

February 27

Ch. 5. 3 and Ch. 7.0 – 7.2 : Expert Systems; Ch. 6 : Knowledge Representation - individual study : Ch. 15 : LISP

March 6

Ch. 8 : Reasoning in Uncertain Situations (Bayesian Nets); REVIEW for MIDTERM -  individual study : Ch. 15: LISP

March 13

SPRING  BREAK

 

 

 

 

make-up class

REVIEW for MIDTERM

individual/group FAQ session

held  in room CS 430A :

Tuesday, March 19 : 3 – 5 PM

Wednesday, March 20 : 5 – 7 PM

March 20

MIDTERM

Covers January 23 – February 27 lectures

March 27

no class –

make-up REVIEW   held  on March 19 and 20

April 3

Ch. 9.0 – 9.3 : Machine Learning : Symbol – Based : Induction (see The Problem of Induction at http://dieoff.org/page126.htm) Concept Learning, Version Spaces and Decision Trees; optional - individual study : MATLAB

April  10

Ch. 10 : Machine Learning : Connectionist – BackPropagation, Clustering and Competitive Learning -  optional - individual study : MATLAB

April 17

Ch. 11 : Machine Learning : Social and Emergent (Genetic Algorithms and Artificial Life); application : data mining and knowledge discovery

April  24

Ch. 12 : Understanding Natural Language; Ch. 16: AI as Empirical Enquiry & Present and Future of AI

May 1

Applications :  Perception, Speech Processing,  Human-Computer Intelligent Interaction, and Biometrics. REVIEW for  FINAL.

Homework # 1 – due February 27

Programming :  Missionaries and Cannibals (use PROLOG)

Three missionaries and three cannibals are on one side of the river,
along with a  boat that can hold one or two people. Find a way to get
everyone to  the other side, without ever leaving a group of missionaries
in one place outnumbered by the cannibals in that place.

Exercises (correspond to January 30 & February 6 – 13  lectures)
Sect. 2.6 (from textbook) : 2, 5 (for  part b look for abduction on p. 304), 6, 10

 

Homework # 2 – due  April 3

Programming : The game of NIM  (input : # of tokens) using minimax (use LISP)
 (see textbook pp. 145 – 146)

Exercises:  (correspond to February 20 lecture)
Sect. 3.5 (from the textbook) : 4, 6, 7
Sect. 4.6 (from the textbook) : 5, 6, 10, 13
Sect. 5.7 (from textbook) : 5, 7, 9

 

Homework # 3 – due May 1

Programming : Classification (use programming language of your choice)
Access UCI repository at www.ics.uci.edu/~mlearn/MLRepository.html  and choose
some classification problem and corresponding data sets. Implement classification using
DT (Decision Trees) or backpropagation (BP) learning. Extra credit if you use both
DT and BP.  Discuss the results and if using both DT and BP, compare the results.

Exercises:  (correspond to February 27 and March 6 lectures)
Sect. 6.6 (from the textbook) : 12 (part a), 13
Sect. 8.5 (from the textbook) : 2

Exercises:  ( correspond to April 3 & 10 lectures)
Sect. 9.9 (from the textbook) : 4, 5