CS313 ARTIFICIAL INTELLIGENCE LABORATORY

Professor Loui

loui--aat--cs.wustl.edu


TA's:  none

Location:  Cupples II-200

Time:  W 5:30-7:30 lecture, W 7:30-8:30 help session/lab

Required Texts:  none

Course Description:  see catalog

3.0 credits

Prerequisites:  programming skill beyond CS102 in any language on any
operating system

		Course in Brief

I pose problems.  Students program solutions.  We discuss approaches.
Students come to understand AI by doing it rather than being told about
it.

Compared to CS511:  CS511 is a graduate course which aims at mastery of
concepts in a book.

Compared to CS517:  CS517 (machine learning) is about well-understood
algorithms for specific kinds of AI problems.

Compared to CS513:  CS513 is more like a group Master's Project in AI,
and is intended to apply AI rather than illustrate it.

Compared to CS101/102:   instead of focusing on programming concepts
and style, exercised through several problems, this course focuses on
programming solutions to various problems.

		Usual Benefits to the Student

Students are forced to use UNIX as a tool and a high standard of
capability is demanded early.  They are exposed to a powerful program
editor and a powerful scripting language and encouraged to use them.

Students exit with an understanding of AI's scope.  They are able to
compare machine learning, optimization, and AI.  They can understand
where search, scoring, compositional syntax, and combinatorial
explosion can happen in their designs and make tradeoffs where needed.
They understand how to incorporate knowledge into a solution and how to
transform it so that it can be usefully represented.  Students see AI
problems, and non-AI problems, in the processing of images, language,
game-playing, and human interaction.

		Usual Student Response

The best programmers tend to love this course.  Or perhaps that is how
I have come to define the best programmers.

Students who expect to be taught about AI explicitly are often
disappointed and should have enrolled in CS511.

Students who do not take quickly to UNIX, scripting, or bottom-up
design are often frustrated by this course.  You must be willing to
program differently from "the right way" which was taught in
CS101/102.  This is exploratory programming, not the production of
industrial code to specification.

		Mechanics

You come to class.  I organize and demonstrate.  We adjourn to the lab,
where I can help people in small groups and often 1-on-1.  Not everyone
is required to attend the lab, but ironically, those who need help the
most are usually the ones who do not attend.  Since almost all of the
work done in this class is done in teams, it is useful to be in the lab
with your teammates even if you do not need help.  There is a strong
correlation between A's in CS313 and lab attendance, but it is not my
intention that there be such a correlation.

We have four to six programming assignments.  Usually four big projects
and two minor checkpoints.  Your grade depends on my assessment of your
performance in those projects (the checkpoints are of secondary
importance).  Where there is a team, I am free to draw my own
conclusions about how to assign credit within the team, and you are
welcome to help me understand how to draw those conclusions.

Your grade depends on the performance of your software and the merit of
your approach.  I am not interested in documentation or programming
style in this class.  You may use any programming language that you can
support (I will not be supporting languages for you) on the UNIX
machines you are required to work with.

Each student shall keep a blog which is the actual graded work in this
class.  Source, scripts, screen grabs, and remarks should be made in
the blog, which is graded occasionally by the professor.  Teams may
post redundant copies of work.  Students will achieve the highest grade
for the briefest mot informative blogging.

		Tentative List of Projects

a.  two-part regular expression exercise

b.  text-mining/web spidering exercise

1.  Negotiating Agent

2.  Classifying Documents

3.  Face Finding

4.  Playing Soccer


Class Meeting  9/1

	This document, above.

	Getting some horrid questions out of the way:

		What is AI?

		Does AI work?

		Can computers be intelligent?  Understand?

		Does AI intend to harm us?

		When will AI be finished?

	Impromptu gawk language examples

		printing, looping, conditionals, functions

		associative arrays, iterators

		a closer look at identifiers and types

		getline from files, from pipes, from stdin

		automatic parsing of input

		two ways to run gawk

		printing to a pipe

		some built-in functions for regexps:  ~, split, index,
		match

		larger structs as strings with delimiters, multi-d
		arrays

		practice with regular expressions