AAAI Listing of Schools – 1999
http://www.cs.cmu.edu/afs/cs.cmu.edu/project/ai-repository/ai/html/faqs/ai/ai_general/ai_1.faq
From: cardo@cs.ucla.edu
Subject: Artificial Intelligence FAQ: Questions & Answers 1/7 [Monthly posting]
Newsgroups: comp.ai,news.answers,comp.answers
Approved: news-answers-request@MIT.EDU
Summary: Frequently asked questions about AI
Distribution: world
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Reply-To: cardo@cs.ucla.edu
Organization: University of California, Los Angeles
Archive-name: ai-faq/general/part1
Posting-Frequency: monthly
Last-Modified: Sat May 29 14:33:13 PST 1999 by Amit Dubey
Version: 2.0
Maintainer: Ric Crabbe <cardo@cs.ucla.edu> and Amit Dubey <adubey@undergrad.math.uwaterloo.ca>
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;;; ****************************************************************
;;; Answers to Questions about Artificial Intelligence *************
;;; ****************************************************************
;;; Maintained by: Amit Dubey <adubey@undergrad.math.uwaterloo.ca>
;;; Ric Crabbe <cardo@cs.ucla.edu>
;;; Written by Ric Crabbe, Amit Dubey, and Mark Kantrowitz
;;; ai_1.faq
If you think of questions that are appropriate for this FAQ, or would
like to improve an answer, please send email to the maintianers.
*** Copyright:
Some portions of this FAQ are Copyright (c) 1992-94 by Mark
Kantrowitz. The rest are Copyright (c) 1999 by Ric Crabbe and Amit
Dubey
*** Disclaimer:
This article is provided as is without any express or implied
warranties. While every effort has been taken to ensure the
accuracy of the information contained in this article, the
author/maintainer/contributors assume(s) no responsibility for
errors or omissions, or for damages resulting from the use of
the information contained herein.
*** Recent changes:
;;; 29-MAY-99 ad modernizing references; heavily updating web/ftp links
;;; 27-MAY-99 rc mention of moderation and moderator
;;; 26-APR-99 rc 2 Questions added.
;;; 20-MAR-99 rc+ad Revived. Many changes
*** Topics Covered:
Part 1:
[1-0] What is the purpose of this newsgroup?
[1-1] What is AI?
[1-2] What's the difference between strong AI and weak AI?
[1-3] I'm a programmer interested in AI. Where do I start?
[1-4] History of AI.
[1-5] What has AI accomplished?
[1-6] What are the branches of AI?
[1-7] What are good programming languages for AI?
[1-8] Glossary of AI terms.
[1-9] What are the top schools in AI?
[1-10] How can I get the email address for Joe or Jill Researcher?
[1-11] What are the rules for the game of "Life"?
[1-12] What AI competitions exist?
[1-13] Commercial AI products.
[1-14] AI Job Postings
[1-15] Future Directions of AI
[1-16] Why is this FAQ so short?
[1-17] Where are the FAQs for...neural nets? natural language?
artificial life? fuzzy logic? genetic algorithms?
philosophy? Lisp? Prolog? robotics?
Part 2 (AI-related Newsgroups and Mailing Lists):
- List of all known AI-related newsgroups, newsgroup archives, mailing
lists, and electronic bulletin board systems.
Part 3 (AI-related Associations and Journals):
- List of AI-related associations and journals, organized by subfield.
Part 4 (Bibliography):
- Bibliography of introductory texts, overviews and references
- Addresses and phone numbers for major AI publishers
- Finding conference proceedings
- Finding PhD dissertations
Part 5 (FTP Resources):
[5-0] General Information about FTP Resources for AI
[5-1] FTP Repositories
[5-2a] FTP and Other Resources: Agents -- Planning
Note: Question [5-2] (FTP and Other Resources) is split across parts 5 and 6.
Part 6 (FTP Resources):
[5-2b] FTP and Other Resources: Qualitative Reasoning -- Theorem Proving
Part 7 (FTP Resources):
[7-1] AI Bibliographies available by FTP
[7-2] AI Technical Reports available by FTP
[7-3] Where can I get a machine readable dictionary, thesaurus, and
other text corpora?
[7-4] List of Smalltalk implementations.
[7-5] AI-related CD-ROMs
[7-6] World-Wide Web (WWW) Resources
Search for [#] to get to question number # quickly.
*** Introduction:
Certain questions and topics come up frequently in the various network
discussion groups devoted to and related to Artificial Intelligence
(AI). This file/article is an attempt to gather these questions and
their answers into a convenient reference for AI researchers. It is
posted on a monthly basis. The hope is that this will cut down on the
user time and network bandwidth used to post, read and respond to the
same questions over and over, as well as providing education by
answering questions some readers may not even have thought to ask.
The latest version of this FAQ is available via anonymous FTP from
ftp://ftp.cs.ucla.edu/pub/AI/
as the files ai_[1-7].faq.
The FAQ postings are also archived in the periodic posting archive on
rtfm.mit.edu:/pub/usenet/news.answers/ai-faq/general/ [18.181.0.24]
If you do not have anonymous ftp access, you can access the archive by
mail server as well. Send an E-mail message to mail-server@rtfm.mit.edu
with "help" and "index" in the body on separate lines for more
information.
----------------------------------------------------------------
Subject: [1-0] What is the purpose of the newsgroup comp.ai?
Comp.ai is a moderated newsgroup whose topic is Artificial Intelligence.
It has existed since the early days of USENET (at least 10 years) and
has been a moderated newsgroup since 5th May 1999. An introduction for
new readers including the official charter, moderation policies and
posting guidelines may be found at <http://www.cs.mu.oz.au/~dnk/comp.ai>.
The current moderator is David Kinny, but the actual moderation is done
largely automatically by an intelligent :-) agent (the AI-mod-bot).
The group is meant for general discussion of AI topics (but not about
those for which specialized subgroups already exist), including:
o announcements of AI conferences, reports, books, products and jobs.
o questions and discussion about AI theory and practice, algorithms,
systems and applications, problems, history and future trends.
o distribution of AI source code (preferably indirectly by weblinks)
All contributions should be of potential interest to the general AI
community, and in English plain text without attachments. See part 2
of this FAQ for a list of other more specialized newsgroups and lists.
Every so often, somebody posts an inflammatory message, such as
Will computers every really think?
AI hasn't done anything worthwhile.
These "religious" issues serve no real purpose other than to waste
bandwidth. If you feel the urge to respond to such a post, please do
so through a private e-mail message, or post redirecting follow-ups to
comp.ai.philosophy. We suspect this will be less of a problem now
that the group is moderated.
We've tried to minimize the overlap with the FAQ postings to the
comp.lang.lisp, comp.lang.prolog, comp.ai.neural-nets, and
comp.ai.shells newsgroups, so if you don't find what you're looking
for here, we suggest you try the FAQs for those newsgroups. These FAQs
should be available by anonymous ftp in subdirectories of
rtfm.mit.edu:/pub/usenet/
or by sending a mail message to mail-server@rtfm.mit.edu with subject "help".
----------------------------------------------------------------
Subject: [1-1] What is AI?
Artificial intelligence ("AI") can mean many things to many people.
Much confusion arises that the word 'intelligence' is ill-defined.
The phrase is so broad that people have found it useful to divide AI
into two classes: strong AI and weak AI.
----------------------------------------------------------------
Subject: [1-2] What's the difference between strong AI and weak AI?
Strong AI makes the bold claim that computers can be made to think on
a level (at least) equal to humans. Weak AI simply states that some
"thinking-like" features can be added to computers to make them more
useful tools... and this has already started to happen (witness expert
systems, drive-by-wire cars and speech recognition software). What
does 'think' and 'thinking-like' mean? That's a matter of much
debate.
----------------------------------------------------------------
Subject: [1-3] I'm a programmer interested in AI. Where do I start?
There's a list of introductory AI texts in the bibliography section
of the FAQ [4-0].
[1-3a] I'm writing a game that needs AI.
It depends what the game does. If it's a two-player board game,
look into the "Mini-max" search algorithm for games (see [4-1]). In
most commercial games, the AI is is a combination of high-level
scripts and low-level efficiently-coded, real-time, rule-bsed
systems.
----------------------------------------------------------------
Subject: [1-4] History of AI.
For an online timeline of artificial intelligence milestones, see
ftp://ftp.cs.ucla.edu/AI/timeline.txt
The appendix to Ray Kurzweil's book "Intelligent Machines" (MIT Press,
1990, ISBN 0-262-11121-7, $39.95) gives a timeline of the history of AI.
Pamela McCorduck, "Machines Who Think", Freeman, San Francisco, CA, 1979.
Allen Newell, "Intellectual Issues in the History of Artificial
Intelligence", Technical Report CMU-CS-82-142, Carnegie Mellon
University Computer Science Department, October 28, 1982.
See also:
Charniak and McDermott's book "Introduction to Artificial Intelligence",
Addison-Wesley, 1985 contains a number of historical pointers.
Daniel Crevier, "AI: The Tumultuous History of the Search for
Artificial Intelligence", Basic Books, New York, 1993.
Henry C. Mishkoff, "Understanding Artificial Intelligence", 1st edition,
Howard W. Sams & Co., Indianapolis, IN, 1985, 258 pages,
ISBN 0-67227-021-8 $14.95.
Margaret A. Boden, "Artificial Intelligence and Natural Man", 2nd edition,
Basic Books, New York, 1987, 576 pages.
----------------------------------------------------------------
Subject: [1-5] What has AI accomplished?
Quite a bit, actually. In 'Computing machinery and intelligence.',
Alan Turing, one of the founders of computer science, made the claim
that by the year 2000, computers would be able to pass the Turing test
at a reasonably sophisticated level, in particular, that the average
interrogator would not be able to identify the computer correctly more
than 70 per cent of the time after a five minute conversation. AI
hasn't quite lived upto Turing's claims, but quite a bit of progress
has been made, including:
- Deployed speech dialog systems by firms like IBM, Dragon and Lernout&Hauspie
- Applications of expert systems/case-based reasoning: a computerized Lukemia
diagnosis system did a better job checking for blood disorders than human
experts!
- Machine translation for Environment Canada: software developed in the 1970s
translated natural language weather forcasts between English and French.
Purportedly stil in use.
- Deep Blue, the first computer to beat the human chess Grandmaster
- Fuzzy controllers in dishwashers, etc.
One persistent 'problem' is that as soon as an AI technique trully
succeeds, in the minds of many it ceases to be AI, becoming something
else entirely. For example, when Deep Blue defeated Kasparov, there
were many who said Deep Blue wasn't AI, since after all it was just a
brute force parallel minimax search (!)
ref:
Alan M. Turing. Computing machinery and intelligence. Mind,
LIX(236):433-460, October 1950.
Sheiber, S, "Lessons from a Restricted Turing Test". Communications of
the Association for Computing Machinery, volume 37, number 6, pages
70-78, 1994
----------------------------------------------------------------
Subject: [1-6] What are the branches of AI?
There are many, some are 'problems' and some are 'techniques'.
Automatic Programming - The task of describing what a program
should do and having the AI system 'write' the program.
Bayesian Networks - A technique of structuring and inferencing
with probabilistic information.
Natural Language Processing(NLP) - Processing and (perhaps)
understanding human ("natural") language
Knowledge Engineering/Representation - turning what we know about
a particular domain into a form in which a computer can
understand it.
Planning - given a set of actions, a goal state, and a present state,
decide which actions must be taken so that the present state
is turned into the goal state
Constraint Statisfaction - solving NP-complete problems, using a
variety of techniques.
Machine Learning - Programs that learn from experience.
Visual Pattern Recognition - The ability to reproduce the
human sense of sight on a machine.
Speech Recogntion - Conversion of speech into text.
Search - The finding of a path from a start state to a goal
state. Similar to planning, yet different...
Neural Networks(NN) - The study of programs that function in a
manner similar to how animal brains do.
AI problems (speech recognition, NLP, vision, automatic programming,
knowledge representation, etc.) can be paired with techniques (NN,
search, Bayesian nets, production systems, etc.) to make distinctions
such as search-based NLP vs. NN NLP vs. Statistical/Probabilistic NLP.
Then you can combine techniques, such as using neural networks to
guide search. And you can combine problems, such as posing that
knowledge representation and language are equivalent. (Or you can
combine AI with problems from other domains.)
----------------------------------------------------------------
Subject: [1-7] What are good programming languages for AI?
This topic can be somewhat sensitive, so I'll probably tread of a few
toes, please forgive me. There is no authoritative answer for this
question, as it really depends on what languages you like programming
in. AI programs have been written in just about every language ever
created. The most common seem to be Lisp, Prolog, C, and recently
Java.
LISP- For many years, AI was done as research in universities and
laboratories, thus fast prototyping was favored over fast execution.
This is one reason why AI has favored high-level langauges such as
Lisp. This tradition means that current AI Lisp programmers can draw
on many resources from the community. Features of the language that
are good for AI programming include: garbage collection, dynamic
typing, functions as data, uniform syntax, interactive environment,
and extensibility.
PROLOG- This language wins 'cool idea' competition. It wasn't until
the 70s that people began to realize that a set of logical statements
plus a general theorem prover could make up a program. Prolog
combines the high-level and tradition advantages of Lisp with a
built-in unifier, which is particularly useful in AI. Prolog seems to
be good for problems in which logic is intimately involved, or whose
solutions have a succinct logical characterization. Its major
drawback (IMHO) is it is hard to learn.
C- The speed demon of the bunch, C is mostly used when the program is
simple, and excecution speed is the most important. Neural Networks
are a common example of this. Backpropagation is only a couple of
pages of C code, and needs every ounce of speed that the programmer
can muster.
Java- The newcomer, Java uses several ideas from Lisp, most notably
garbage collection. Its portability makes it desirable for just about
any application, and it has a decent set of built in types. Java is
still not as high-level as Lisp or Prolog, and not as fast as C,
making it best when portability is paramount.
(some of the above material is due to the comp.lang.prolog FAQ, and
Norvig's "Paradigms of Artificial Intelligence Programming: Case
Studies in Common Lisp")
----------------------------------------------------------------
Subject: [1-8] Glossary of AI terms.
This is the start of a simple glossary of short definitions for AI
terminology. The purpose is not to present the gorey details, but
give ageneral idea.
A*:
A search algorithm to find the shortest path through a search
space to a goal state using a heuristic. See 'search',
'problem space', 'Admissibility', and 'heuristic'.
Admissibility:
An admissible search algorithm is one that is guaranteed to
find an optimal path from the start node to a goal node, if
one exists. In A* search, an admissible heuristic is one that never
overestimates the distance remaining from the current node to
the goal.
Agent:
"Anything that can can be viewed a perceiving its environment
through sensors and acting upon that environment through
effectors." [Russel, Norvig 1995]
ai:
A three-toed sloth of genus Bradypus. This forest-dwelling
animal eats the leaves of the trumpet-tree and sounds a
high-pitched squeal when disturbed. (Based on the Random House
dictionary definition.)
Alpha-Beta Pruning:
A method of limiting search in the MiniMax algorithm. The
coolest thing you learn in an undergraduate course.
Backward Chaining:
In a logic system, reasoning from a query to the data. See
Forward chaining.
Belief Network (also Bayesian Network):
A mechanism for representing probabilistic knowledge.
Inference algorithms in belief networks use the structure of
the network to generate inferences effeciently (compared to
joint probability distributions over all the variables).
Breadth-first Search:
An uninformed search algorithm where the shallowest node in
the search tree is expanded first.
Case-based Reasoning:
Technique whereby "cases" similar to the current problem are
retrieved and their "solutions" modified to work on the current
problem.
Closed World Assumption:
The assumption that if a system has no knowledge about a
query, it is false.
Data Mining:
Also known as Knowledge Discovery in Databases (KDD) was been defined
as "The nontrivial extraction of implicit, previously unknown, and
potentially useful information from data" in Frawley and
Piatetsky-Shapiro's overview. It uses machine learning, statistical
and visualization techniques to discover and present knowledge in a
form which is easily comprehensible to humans.
Depth-first Search
An uninformed search algorithm, where the deepest non-terminal
node is expanded first.
Evaluation Function:
A function applied to a game state to generate a guess as to
who is winning. Used by Minimax when the game tree is too
large to be searched exhaustively.
Forward Chaining:
In a logic system, reasoning from facts to conclusions. See
Backward Chaining
Fuzzy Logic:
In Fuzzy Logic, truth values are real values in the closed
interval [0..1]. The definitions of the boolean operators are
extended to fit this continuous domain. By avoiding discrete
truth-values, Fuzzy Logic avoids some of the problems inherent in
either-or judgments and yields natural interpretations of utterances
like "very hot". Fuzzy Logic has applications in control theory.
Iterative Deepening:
An uninformed search that combines good properties of
Depth-fisrt and Breadth-first search.
Iterative Deepening A*:
The ideas of iterative deepening applied to A*.
Machine Learning:
A field of AI concerned with programs that learn. It includes
Reinforcement Learning and Neural Networks among many other
fields.
MiniMax:
An algorithm for game playing in games with perfect
information. See alpha-beta pruning.
Modus Ponens:
An inference rule that says: if you know x and you know that
'If x is true then y is true' then you can conclude y.
Nonlinear Planning:
A planning paradigm which does not enforce a total (linear)
ordering on the components of a plan.
Partial Order Planner:
A planner that only orders steps that need to be ordered, and
leaves unordered any steps that can be done in any order.
Planning:
A field of AI concerned with systems that constuct sequences
of actions to acheive goals in real-world-like environments.
Problem Space (also State Space):
The formulation of an AI problem into states and operators.
There is usually a start state and a goal state. The problem
space is searched to find a solution.
Search:
The finding of a path from a start state to a goal state. See
'Admissibility', 'Problem Space', and 'Heuristic'.
Strong AI:
Claim that computers can be made to actually think, just like human
beings do. More precisely, the claim that there exists a class of
computer programs, such that any implementation of such a program is
really thinking.
Unification:
The process of finding a substitution (an assignment of
constants and variables to variables) that makes two logical
statements look the same.
Validation:
The process of confirming that one's model uses measureable inputs
and produces output that can be used to make decisions about the
real world.
Verification:
The process of confirming that an implemented model works as intended.
Weak AI:
Claim that computers are important tools in the modeling and
simulation of human activity.
----------------------------------------------------------------
Subject: [1-9] What are the top schools in AI?
note: The answer to this question is clearly out of date. Any help
would be appreciated.
The answer to this question is not intended to be a ranking and should
not be interpreted as such. There are several major problems with
ratings like the Gourman Report and the US News and World Report. Such
rankings are often unsubstantiated and anecdotal, their accuracy is
questionable, and they do not focus on the subfields of an area. When
selecting a graduate school, students should look for schools which
not only have excellent programs in their general area of research
but also at least one faculty member whose research interests mesh
well with the student's. Accordingly, we've broken down this list
according to topic, and sorted the schools within each topic in
ALPHABETICAL ORDER.
For a school to be added to a topic area, there should at least two
faculty actively conducting research in that area and the school
should have a "good" reputation in that area. Exceptions are made for
schools which only have one faculty member in the area, but that
professor is a "leader" of the area, or for fields where the total
number of people working in the area is small in the first place. The
general idea behind these criteria is to ensure that a school has
enough activity in the area that a student who considers one of these
schools won't be disappointed if one of the faculty in that area is on
sabbatical or isn't taking students. Note that the research need not
be conducted in the school's computer science department for the
school to be listed -- in some cases we've included schools where the
research is being conducted in a different department or special laboratory.
The best way for students to discover which schools are good in a
field is to ask professors (and graduate students) in their
undergraduate school for suggestions on where to apply. Reading the
research journals in the field is another good method (see part 3 of
the FAQ).
A genealogy of AI thesis-advising relationships is available by
anonymous ftp as
cs.ucsd.edu:/pub/rik/aigen.rpt
[Maintainers' note: this seems to be no longer available]
Although intended to complement citation analysis and free-text
information retrieval as tools for understanding the AI community and
their connections to other disciplines, it may be useful to
prospective graduate students. For example, it may help you understand
the historical context of a given professor's perspective. 2,600 MS
and PhD theses have been tabulated so far. If you'd like to
contribute additional listings (including year, title, abstract,
school, advisor, committee members, and subsequent employment), write
to Rik Belew <rik@cs.ucsd.edu> or fax 619-534-7029, for the
questionnaire. A copy of the questionaire and more information is
available in
cs.ucsd.edu:/pub/rik/announce.t
[Maintainers' note: this seems to be no longer available]
A list of email addresses for CS departments is posted once a month to
the newsgroup soc.college.gradinfo.
The Association for Computational Linguistics publishes a directory of
graduate programs in Computational Linguistics ($15 for members, $30
for others). It includes several useful indices (e.g., index of
faculty and a list of references). Contact Association for
Computational Linguistics, Walker, C. N. 925, Bernardsville, NJ
07924-0925, phone/fax 908-204-1337, or send email to acl@bellcore.com.
NOTE THAT THIS LIST IS PRELIMINARY AND BY NO MEANS COMPLETE.
Please feel free to suggest schools that are particularly strong in
any of these areas, or to suggest new areas to be listed.
Schools with excellent programs in most fields:
Carnegie Mellon University (CMU)
MIT
Stanford
Georgia Tech
Imperial College
Indiana
Institute for the Learning Sciences, Northwestern University (ILS)
Johns Hopkins University
Maryland
Rutgers
SUNY/Buffalo
Toronto
UC/Berkeley
UCLA
Univ. of Edinburgh
Univ. of Illinois/Urbana-Champaign (UIUC)
Univ. of Maryland/College Park
Univ. of Massachusetts/Amherst
Univ. of Michigan
Univ. of Pennsylvania
Univ. of Pittsburgh
Univ. of Rochester
Univ. of Southern California & USC/Information Sciences Institute
Univ. of Sussex, School of Cognitive and Computing Sciences
Univ. of Texas/Austin
Yale
Universities with 2 or more AAAI Fellows:
Note: Some Fellows have changed their affiliation since being named,
so this list isn't completely accurate.
12 MIT
12 Stanford University
10 Carnegie Mellon University (CMU)
7 Univ. of Massachusetts
7 Univ. of Southern California (USC) + Information Sciences Institute
5 Univ. of Toronto
5 Univ. of Pennsylvania
5 Rutgers
4 Univ. of Maryland
4 Univ. of Texas at Austin
3 Northwestern
3 Univ. of California, Berkeley
3 Univ. of Edinburgh
3 Univ. of Illinois
3 Univ. of Pittsburgh
2 Brown University
2 Duke University
2 Harvard
2 UCLA
2 Univ. of Rochester
2 Univ. of Sydney
Universities with one AAAI Fellow include: Columbia University,
George Mason, Georgia Tech, Imperial College, New Mexico State,
Ohio State, Oregon State University, Oxford, P. and M. Curie
University, SUNY/Binghamton, SUNY/Buffalo, Saint Joseph, San Jose
State, Syracuse, Tufts, UC Irvine, UC/Santa Cruz, UCSD, Univ. of
Birmingham, Univ. of British Columbia, Univ. of Cambridge, Univ. of
Linkoeping, Univ. of Marseille, Univ. of Minnesota, Univ. of
Sussex, Wellesley, Yale
The full list of AAAI Fellows and their affiliations is available
from AAAI at: http://www.aaai.org/Fellows/fellows-list.html
Specialties and Universities:
AI and Manufacturing:
Carnegie Mellon University (CMU) -- CIMDS
Univ. of Maryland/College Park
Univ. of Toronto
AI and Medicine:
MIT
Stanford
Univ. of Pittsburgh
Univ. of Birmingham England (School of Computer Science)
AI and Legal Reasoning:
Imperial College
Univ. of Massachusetts/Amherst
Artificial Life:
MIT (Brooks' mobots)
NYU
Santa Fe Institute (SFI)
Stanford
UC Santa Cruz
UCLA
UCSD
Univ. of Birmingham England (School of Computer Science)
Univ. of Delaware
Univ. of Sussex, School of Cognitive and Computing Sciences
Automated Deduction/Theorem Proving:
Imperial College
Stanford
Univ. of Edinburgh
Univ. of Oregon
Univ. of Texas/Austin
Case-Based Reasoning/Analogical Reasoning:
Chicago
Georgia Tech
Institute for the Learning Sciences, Northwestern University (ILS)
Univ. of Massachusetts/Amherst
Univ. of Pittsburgh
Cognitive Modelling:
Carnegie Mellon University (CMU)
Georgia Tech
Indiana
SUNY Buffalo
Univ. of Birmingham England (School of Computer Science)
Univ. of Maryland/College Park
Univ. of Michigan
Cognitive Science:
Brown University
Carnegie Mellon University (CMU)
Georgia Tech
Indiana University/Bloomington
Johns Hopkins
MIT
Princeton
Rutgers
SUNY/Buffalo
Stanford
UC/Berkeley
UC/San Diego
Univ. of Birmingham England (School of Computer Science)
Univ. of Colorado/Boulder
Univ. of Edinburgh
Univ. of Minnesota
Univ. of Pennsylvania
Univ. of Rochester
Univ. of Sussex, School of Cognitive and Computing Sciences
Computational Biology:
Carnegie Mellon University
Johns Hopkins University
Rutgers
UC/Berkeley
Univ. of Birmingham England (School of Computer Science)
Univ. of Pennsylvania
Univ. of Wisconsin/Madison
Computer Vision: See Machine Vision
Connectionism/Neural Networks:
Boston University, Cognitive and Neural Systems Department (ART networks)
Brown University
CalTech
Carnegie Mellon University (CMU)
Helsinki University of Technology, Finland
Indiana
Johns Hopkins University
MIT
Ohio State Univ.
Stanford
Syracuse University
Texas A&M
Toronto
UC/Berkeley
UC/Irvine
UC/San Diego
UCLA
UNC/Chapel Hill
Univ. of Birmingham England (School of Computer Science)
Univ. of Colorado/Boulder
Univ. of Edinburgh
Univ. of Maryland/College Park
Univ. of Massachusetts/Amherst
Univ. of Pennsylvania
Univ. of Southern California & USC/Information Sciences Institute
Univ. of Sussex, School of Cognitive and Computing Sciences
Univ. of Wisconsin
Decision Theory and AI:
Berkeley
MIT
Stanford
Univ. of Michigan
Univ. of Washington
Distributed AI:
Georgia Institute Of Technology
MIT
Nova Southeastern University
Stanford University
Univ. of Maryland
Univ. of Massachusetts/Amherst
Univ. of Michigan
Emotion:
Carnegie Mellon University
Institute for the Learning Sciences, Northwestern University (ILS)
Univ. of Birmingham England (School of Computer Science)
Fuzzy Logic:
Berkeley
Univ. of Birmingham England (School of Computer Science)
Genetic Algorithms:
George Mason Univ.
Indiana
Stanford (Koza)
UC San Diego
UCLA
Univ. of Birmingham England (School of Computer Science)
Univ. of Illinois/Urbana-Champaign (UIUC)
Univ. of Michigan
Univ. of Sussex, School of Cognitive and Computing Sciences
Integrated AI Architectures/Software Agents:
Carnegie Mellon University (CMU)
Stanford
Univ. of Birmingham England (School of Computer Science)
Univ. of Michigan
Univ. of Sussex, School of Cognitive and Computing Sciences
Intelligent Tutoring, AI & Education:
Carnegie Mellon University (Cognitive Science Department)
Illinois Institute of Technology (IIT)
Institute for the Learning Sciences, Northwestern University (ILS)
Univ. of Birmingham England (School of Computer Science)
Univ. of Pittsburgh
Univ. of Sussex, School of Cognitive and Computing Sciences
Knowledge Representation:
Institute for the Learning Sciences, Northwestern University (ILS)
Stanford
SUNY/Buffalo
Univ. of Birmingham England (School of Computer Science)
Univ. of Maryland/College Park
Univ. of Oregon
Logic Programming and Logic-based AI:
Carnegie Mellon University (CMU)
Imperial College
Stanford
UCLA
Univ. of Edinburgh
Univ. of Maryland/College Park
Univ. of Melbourne
Univ. of Illinois/Urbana-Champaign (UIUC)
Univ. of Oregon
Univ. of Pennsylvania
Machine Discovery:
Carnegie Mellon University (CMU)
Univ. of Birmingham England (School of Computer Science)
Machine Learning:
Brown University
Carnegie Mellon University (CMU)
George Mason
Georgia Tech
Johns Hopkins University
MIT
UCI
Univ. of Massachusetts/Amherst
Univ. of Michigan
Univ. of Southern California & USC/Information Sciences Institute
Univ. of Texas/Austin
Univ. of Wisconsin
Waterloo
Machine Vision:
Carnegie Mellon University (CMU)
Columbia
Johns Hopkins
MIT
Oxford
SUNY/Buffalo
UCLA
UNC/Chapel Hill
Univ. of Birmingham England (School of Computer Science)
Univ. of Edinburgh
Univ. of Maryland/College Park
Univ. of Massachusetts/Amherst
Univ. of Rochester
Univ. of Southern California & USC/Information Sciences Institute
Univ. of Sussex, School of Cognitive and Computing Sciences
Univ. of Wisconsin
Natural Language Processing (NLU, NLG, Parsing, NLI, Speech):
Brown
Carnegie Mellon University (CMU)
Columbia
Georgia Tech
Illinois Institute of Technology (IIT)
Institute for the Learning Sciences, Northwestern University (ILS)
ISI
Indiana
Johns Hopkins University
MIT
Oregon Graduate Institute of Science and Engineering
Penn
Rutgers
Stanford
SUNY/Buffalo
Toronto
UCLA
Univ. of Birmingham England (School of Computer Science)
Univ. of Edinburgh
Univ. of Maryland/College Park
Univ. of Massachusetts/Amherst
Univ. of Pittsburgh
Univ. of Rochester
Univ. of Southern California & USC/Information Sciences Institute
Univ. of Sussex, School of Cognitive and Computing Sciences
Waterloo (stylistics, MT, discourse)
Nonmonotonic Reasoning:
Imperial College
Stanford
UCLA
Univ. of Birmingham England (School of Computer Science)
Univ. of Maryland/College Park
Univ. of Oregon
Toronto
Philosophy of AI:
Berkeley
MIT
SUNY Buffalo
Univ. of Birmingham England (School of Computer Science)
Univ. of Maryland/College Park
Univ. of Sussex, School of Cognitive and Computing Sciences
Planning:
Brown University
Carnegie Mellon University (CMU)
Imperial College
MIT
Stanford
SUNY Buffalo
Univ. of Birmingham England (School of Computer Science)
Univ. of Maryland/College Park
Univ. of Massachusetts/Amherst
Univ. of Oregon
Univ. of Pittsburgh
Univ. of Rochester
Univ. of Washington/Seattle
Waterloo
Production Systems/Expert Systems:
Carnegie Mellon University (CMU)
Illinois Institute of Technology (IIT)
Stanford
Univ. of Birmingham England (School of Computer Science)
Qualitative Physics and Model Based Reasoning:
Northwestern ILS (Forbus)
Univ. of Oregon
Univ. of Texas/Austin
Univ. of Washington
Reasoning Under Uncertainty (Probabilistic Reasoning, Approximate
Reasoning, etc.):
Brown University
George Mason
Oregon State University
Stanford
UCLA
Univ. of Maryland/College Park
Univ. of Rochester
University of South Carolina
Robotics:
Bristol Polytechnic, UK
Brown
California Institute of Technology (Caltech)
Carnegie Mellon University (CMU)
Georgia Tech
Harvard
Hull University, UK
Johns Hopkins University
MIT
Naval Postgraduate School
New York University (NYU) Courant Institute of Mathematical Sciences
North Carolina State Univerisity/Raleigh (NCSU)
Oxford
Purdue
Reading University, UK
Rennsalear Polytechnic Institute (RPI)
Salford University, UK
Stanford
Swiss Federal Institute of Technology
UC/Berkeley
Univ. of Alberta
Univ. of Edinburgh
Univ. of Kansas
Univ. of Kentucky
Univ. of Maryland/College Park
Univ. of Massachusetts/Amherst
Univ. of Michigan
Univ. of Paris INRIA
Univ. of Pennsylvania
Univ. of Southern California & USC/Information Sciences Institute
Univ. of Utah
Univ. of Wisconsin