CS440 Introduction to Artificial Intelligence Fall 2001 Department of Computer Science

What's New?

Nov. 29: Project presentation shedule is being added to the Semester Project section of this page.

Nov. 29: The file assignment4.problem2.help.txt has been added to help you with the last assignment.

Nov. 26: The file understanding.prolog.txt has been added to help you through Prolog.

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Contents

• What's New?
• Weekly Schedule and Assignments
• Course Description
• Time and Place
• Instructors
• Text Books
• Grading
• Semester Project
• On-Line Resources

Weekly Schedule and Assignments

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Course Description

The course objectives are to learn symbolic computation using Common Lisp and Prolog, to practice techniques for programming AI applications, and to introduce the fundamental theories, algorithms and representational structures underlying Artificial Intelligence.

Class discussions will range from Lisp and Prolog programming fundamentals to philosophical issues in Artificial Intelligence. Lisp and Prolog are used to illustrate basic data structures and programming techniques in AI. Common Lisp and Prolog programs implementing problem-solving search methods and logical reasoning techniques will be studied and modified. Other topics will be covered as time permits. Students must complete a number of written and programming assignments and a semester project. Class size permitting, during the last week of class, semester projects will be presented by students.

As prerequisites for this class, you must be familiar with data structures as taught in CS253 and formal languages and automata as taught in CS301. Familiarity with Common Lisp and Prolog will make programming assignments much easier, but is not required.

Students will have access to the Allegro Common Lisp and SBProlog environments to do their homework and projects. Other common lisp and prolog environments are available. Allegro Common Lisp is a commercial product and SBProlog is public domain. Details on the use of these environments on the computer science network at CSU will be discussed in class. Code you turn in must run in these environments. However, you may develop your assignment solutions in other common lisp and prolog environments. A number of them are available for Macs and PCs, some of which are free or shareware. See the On-Line Resources below.

Student responsibilities include the following. Completion of reading and homework assignments by the beginning of class on the day they are due. Completion of programming assignments requires handing in printouts of programs, data, and results, and possibly electronically checking them in. Unless prior arrangements have been made with the professor (to address special situations) or documentation of an emergency situation is presented, late written assignments will receive no credit and programming assignments will incur a 10% penalty per class day that they are late. Please read the departmental policy statement regarding incompletes, cheating, and class attendance.

The class will be a mix of lecture and discussion. Students are expected to voice all of their questions and to be ready to discuss the reading material.

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Time and Place

• Tuesdays and Thursdays, 12:30 - 1:45, in Wagar 132.

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Instructors

Instructor: Chuck Anderson

• E-mail: anderson@cs.colostate.edu
• Phone: 970-491-7491. FAX: 970-491-2466
• Office: 225 USC. Office Hours: Tuesdays and Thurdays, 10:00 AM - 12:00, and by appointment

Graduate Teaching Assistant: Jean-Luc Romano

• E-mail: romano@cs.colostate.edu
• Phone: (970) 491-6228
• Office: W4 Cubicle USC (room 200)
• Office hours: 8:00 am - 9:00 am Wednesday through Friday in the third floor's North lab

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Text Books

• Required:
  • Artificial Intelligence: A Modern Approach by Russell and Norvig.
• Suggested:
  • ANSI Common Lisp by Paul Graham.

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Grading

• 30% Homework Assignments
• 30% Quizzes
• 10% Midterm Exam
• 10% Final Exam (not comprehensive)
• 20% Project

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Semester Project

Project proposals must be turned in by September 20th. Proposals must be a typed description, at least two pages long, of the project's objectives, method, and expected results. Proposals with instructor's comments will be returned September 25th. Updated proposals are due September 27th and on this day all students will briefly present their proposals in front of class.

Drafts of the final, written project reports are due on December 6th. The final versions of the written reports must be at least 15 pages long. They are due on December 12th, at 4:00 PM. If time permits, near the end of the semester during scheduled class time or other agreed upon time, in-class presentations of the projects will be given. Details on the structure of the written report and of the presentations will be dicussed in class.

Here are some suggestions in postscript about how to structure the report for your semester project. Here is the LaTeX file that generated the postscript file. To produce postscript from this file, you will also need to download this silly figure. To latex it, just do

latex report  (assuming your report is in report.tex)
latex report  (do it again so latex can read the table of contents and
               references generated by the first pass)
dvips -o report.ps report.dvi
gv report.ps 
  or
lp report.ps

pdflatex report
acroread report.pdf

Here is the schedule for presentations on December 8, Room 310B USC.

Time	Name	Title		Time	Name	Title
9:00-9:10	Aaron Webb	Reinforcement Learning		1:00-1:10	Dustin Perkins	Artificial Life
9:10-9:20	Dan Elliott	Checkers Playing Program		1:10-1:20	Kris Johnson	Statistical Simulations
9:20-9:30	Andy Jenkins	Cognitive Science		1:20-1:30	Aaron Alexander	Artificial Life and Genetic Algorithms
9:30-9:40	Al Lionelle	Grand Challenge of AI		1:30-1:40	Jeremy Bradley	AI in space exploration
9:40-9:50	Carolyn Torpy	AI: Fact vs. Fiction		1:40-1:50	Tuan Bui	Machine Replication
9:50-10:00	Troy Heithecker	Fuzzy Logic		1:50-2:00	Chuck Sharp	Planning for Dummies
10:00-10:10				2:00-2:10	Aaron Galuzzi	Creating Intelligent Machines
10:10-10:20	Ben Thomas	Game AI		2:10-2:20	Mike Brake	Genetic algorithms for the game RoboRally
10:20-10:30	Russell Voss	Constraint Satisfaction Problems relating to NLSI		2:20-2:30	Ivan Drinks	Expert Systems
10:30-10:40	Jeff Hill	AI in the Customer Service Domain		2:30-2:40	Adam Lorber	AI involvement in NCAV's
10:40-10:50	Jason Kauffman	Philosophy of AI		2:40-2:50
10:50-11:00	Emel Meadors	Socially Intelligent Agents		2:50-3:00	Thomas Conklin	Neural Networks
11:00-11:10	Chris Hamp	N-Queens		3:00-3:10	Ryan Rohlman	KDD vs. Data Mining
11:10-11:20	Renelle Kelleher	Remote Sensing Using Neural Networks		3:10-3:20	Jeremiah Bush	Sparky the Cricket
11:20-11:30	Hector Aguilar	Computer Vision		3:20-3:30	Carol Kaito	K-means algorithm
11:30-11:40	Tai Xin	Data Clustering		3:30-3:40	Jochen Deyke	Game AI
11:40-11:50	Chauncy McCaughey	AI use in credit card fraud detection		3:40-3:50
11:50-12:00	Wendell Stack	Autonomous Vehicle Navigation		3:50-4:00
				4:00-4:10
				4:10-4:20
				4:20-4:30
				4:30-4:40	Mark Roberts	Cascade Correlation Neural Networks
				4:40-4:50	Karn Sukwong	Agent-Based Virtual Market
				4:50-5:00

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On-Line Resources

• LISP:
  • How to Run Allegro Common Lisp on the CS department's Solaris workstations. Also, here is a brief summary of the steps you must take to set yourself up to run Allegro Common Lisp
    
  • Allegro Common Lisp with Emacs: Lines to add to your .emacs file and a tutorial on using Allegro Common Lisp from within Emacs. Also, here is extensive documentation from Franz about the emacs-lisp interface, and here is a handy reference card for the emacs-lisp interface. (Don't overlook the reference card for just emacs listed below in the emacs section.
    (If you have trouble with the above Lines link when you use xemacs, type the following:

       alias xemacs /usr/local/bin/xemacs

    You may want to add this line to your .cshrc file so you won't have to type it in every single time you log on to use Allegro Common Lisp with xemacs.)
  • Common Lisp, The Language Guy Steele's Common Lisp book on-line
    
  • The loop macro explanation from Steele's book.
  • XANALYSIS LispWorks, a free common lisp environment for Linux and Windows.
  • Powermac Lisp with on-line help and parenthesis balancing
  • Franz Inc., the makers of Allegro Common Lisp, the lisp we are using in class. This site is where you can download a free trial version of Allegro Common Lisp, take a nice on-line Lisp tutorial, and read some comments about the advantages of Lisp.
  • Lisp pointers and code examples from Paul Grahams's "ANSI Common Lisp" and "On Lisp" books
  • On-line Lisp tutorials
    • Marty Hall's Learning Common Lisp Page. This is where those short descriptive handouts came from!
    • ELM-ART, an interactive  Lisp Tutor. You can do the examples , and the program will critique your work! Uses a fancy interactive WWW server written in Lisp, what else :-)
    • Colin Allen's Common Lisp Primer (An HTML Introductory lisp textbook)
• Prolog:
  • Introduction to Prolog, a short introduction with exercises you do interactively. Also includes link to a complete on-line book called Prolog Programming A First Course, by Paul Brna.
  • XSB Prolog manual
  • examples of code are available at /usr/local/XSB/examples
  • interesting recent papers and talks on prolog and logical reasoning are available from Peter Flach,
  • run prolog by typing xsb -i from mozart, beethoven or any Sun machine. The initialization file defines three relations: vi, emacs, and append.
  • Prolog for Windows 95 Other versions available at that site.
  • Web site with pointers to shareware Prolog for IBM PC and Macintosh
• Emacs:
  • Main site for GNU emacs.
  • Emacs Manual, available from above site.
  • Reference card for emacs. A two-page postscript file of the most useful emacs commands.
  • Emacs Basic Emacs Editor Commands.
• Fun stuff
  • Article on AI in USA Today, from June 20, 2001.
  • Turing's Prophecy Machine Intelligence: The First 100 Years (1940-2040) by Raymond Kurzweil. Also check out his Timeline of past and future AI.
  • RoboCup web page describing the RoboCup competition with pointers to code!
  • Xavier the robot your can command on-line
  • Software Agents repository of information about state of the art in intelligent software agents
  • Shareware and Freeware Neural Network Software
• Bibliographies and Sources of Literature
  • The Collection of Computer Science Bibliographies
  • The Online bibliography on planning and scheduling
  • AI Subject Index
  • UCI Machine Learning Repository
  • Page full of pointers to machine learning bibliographies, data, software, etc.
  • Pointers to AI web sites
  • Scientific Research index with a bias toward CS and AI
• Writing and Speaking Resources
  • Check the Writing Center web site for tutorials and pointers on improving your writing.
  • "Pointers on Giving a Talk," D. Messerschmitt
  • The Short Talk Charles Van Loan,
  • Tips for Preparing Scientific Presentation ONR
• Latex help
  • TeX and LaTeX Starter Page, a list of links to very helpful sites for beginners. The most helpful is the The (Not So) Short Introduction to LaTeX2e. This is long; you may save it on your computer to refer to later. The CTAN home page linked here is the home site for the Comprehensive TeX Archive Network where many other helpful documents and extensions for LaTeX are available.
  • LaTeX Tutorial, includes information for using latex on MS Windows, using vi to edit latex, and some nice on-line help. In particular, check out their reference site.
  • Latex tutorial that is short and quickly shows you all you need to know to produce a report. Also includes advice on how to write a report.
  • Latex Tutorial with an introduction to using the Auctex package in emacs for editting and viewing Latex files.

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