Professor
Harry Wechsler
Department
of Computer Science
e-mail : wechsler@cs.gmu.edu
www: http://cs.gmu.edu/~wechsler/
(703)993-1533
(office)
(703)993-1530
(sec)
(703)993-1710
(fax)
____________________________________________
SPRING 2005
Artificial Intelligence
Office Hours
M
Teaching Assistant
Shen - Shynag Ho, Tu
2 – 4 pm and Th 3 – 5 pm,
rm. 365 [ST2] – sho@gmu.edu
Textbook
Artificial
Intelligence: A Modern Approach by Stuart Russell and Peter Norvig,
2nd
edition, Prentice Hall, 2003.
Web site: http://aima.cs.berkeley.edu/
http://aima.eecs.berkeley.edu/slides-pdf/
Reference -
Artificial
Intelligence (4th Edition) by
George Luger, Addison Wesley, 2002
Course
Description
The course is about
the automation and learning of intelligent behavior. We cover basic principles
and methods for intelligent search and problem solving, game
playing, and constraint satisfaction problems (CSP), knowledge representation, predicate calculus /
first-order logic and automatic reasoning (resolution with refutation),
reasoning with uncertainty and belief (Bayesian) networks, (symbolic,
connectionist and evolutionary) learning, and natural language processing [communication]. LISP, PROLOG, and MATLAB are the AI
programming languages of choice used to implement the methods learned during
the course. The approach used throughout the course is to address specific
intelligence tasks, motivate how to solve them, describe algorithmic solutions,
and undertake comparative performance evaluation.
Programming
Languages
Here are some links for LISP, PROLOG and MATLAB :
On osf1, there's a Lisp system called LispWorks. If you don't like telnetting
in and running Lisp :-) there are several freeware packages you can try. Several
Major Lisp firms offer free ANSI-standard CLTL2 common Lisp systems:
You should be aware that there are two other variants of Lisp out there which are NOT Common Lisp. Those variants are Scheme and Emacs Lisp.
2.
Getting Prolog
SWI-Prolog
http://aima.cs.berkeley.edu/ai.html#prolog
3.
Getting MATLAB
MATLAB primer available at:
http://www.math.utah.edu/lab/ms/matlab/matlab.html
access to MATLAB from both CS and IT&E
for further information use 'help' and 'demo'
Grading
1.
Homework à 20 % {HW#1 – CSP
– due date March 7; HW#2 – FOL – due date March 21}
2. Projects à 40 %
You have to submit
Project #1 and one of Projects #2 or #3 (described below after the tentative
schedule for the class). You have to use at least one of the AI programming
languages on the projects you choose.
Project
#1 à due on or before March 28 à 20 %
Project
#2 à due on or before April 18 à 20 %
Project #3 à due on or before April 29 à 20 %
2.
EXAM#1: March
21 à 20 %
3.
EXAM#2: May
16 à 20 %
Tentative
Schedule
|
January 24 |
Chs.1 and 2: = AI. History and Applications. Is the Brain a Digital Computer by J. R. Searl: http://cogsci.soton.ac.uk/~harnad/Papers/Py104/searle.comp.html
|
|
January 31 - February 14 |
Strategies for State Space Search (minmax
and alpha-beta pruning), Informed and Heuristic Search Methods, Game Playing,
Constraint Satisfaction Problems (CSP), Evolutionary Computation and Genetic
Algorithms. applications: game design LISP: (reference: Luger / Ch. 15) |
|
February 21 - March 21 |
Chs. 7 - 9 := Logic and Reasoning. Propositional
Logic, First Order Logic and Predicate Calculus, Reasoning and Inference;
Resolution (with refutation) (mechanical) Theorem Proving. REVIEW for EXAM 1 PROLOG:
(reference: Luger / Ch. 14) |
|
March 14 |
Spring
Break |
|
March 21 |
EXAM#1 - CLOSED BOOKS and CLOSED NOTESPlease bring the
blue examination book! Covers January 24 - March 7 lectures. |
|
March 28 - April 4 |
|
|
April 4 – April 11 |
Chs. 18
& 20:= (Machine) Learning.
Symbolic Based:
Induction (The Problem of Induction at http://dieoff.org/page126.htm)
Decision Trees (DT); Connectionist Based: Perceptron;
Multi-Layer Networks & BackPropagation
(BP); k-means clustering; Performance
Evaluation. applications : data mining and knowledge discovery MATLAB (reference: MATLAB primer) |
|
April 11 - April 18 |
Chs. 13 - 14: = Uncertainty Bayesian Nets.
/download notes http://www.cs.ubc.ca/~murphyk/Bayes/bnintro.html applications: decision-making systems |
|
April 25 – May 2 |
applications : augmented cognition, HCI, and biometrics |
|
May 2 |
Chs. 26 - 27:= Conclusions; REVIEW for EXAM#2
|
|
May 16 |
EXAM #2 –
FINAL -- covers March
28 – April 25 lectures - Please bring the
blue examination book! Please bring scientific
calculator with logarithmic function |
Project # 1 - due on or before March
28: Search and Problem Solving / Game
Playing / CHECKERS or
another game of your choice/ Use programming language of your choice/
Game
Rules:
distributed in class.
Use intelligent
search and implement a user interface to play the game.
Schedule time to
have your program play against the Instructor or TA &
Submit Short
Report that includes (i) task and approach; (ii)
representation, data structures, and GUI; (iii) game strategy (look-ahead, minmax, alpha-beta) and evaluation function; (iv)
information on software tools used and hardware platform; and (v) performance evaluation
and conclusions.
Project # 2 - due on or before April 18 : Knowledge
Representation and Reasoning
Programming
1: Missionaries and
Cannibals / Use LISP or PROLOG or both for extra 10 % credit /
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. Try using CSP
strategies.
OR
Programming
2: your choice of
problem / Use LISP or PROLOG or both for extra 10
% credit /
Project # 3 - due on or before May 2 : Learning
Programming
1:
Classification / Use MATLAB /
Access UCI
repository at www.ics.uci.edu/~mlearn/MLRepository.html and choose
some classification problem and the corresponding data sets. Solve and implement the classification task
using DT (Decision Trees). Discuss your results. For extra credit (10 %)
solve and implement the same classification task using BackPropagation
(BP) and make a comparison against the results obtained using DT.