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发信人: tang (独孤九剑〖玄铁重剑〗), 信区: Program
标 题: What is AI?(转)
发信站: BBS 荔园晨风站 (Sat Dec 4 14:25:01 1999), 转信
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.
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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 muchdebate.
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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.
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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.
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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, pages70-78, 1994
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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.)
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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 recentlyJava.
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 programmercan 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")
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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 ofthe FAQ).
A genealogy of AI thesis-advising relationships is available byanonymous 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 isavailable 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 YaleUniversities 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 PittsburghCognitive 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 MichiganCognitive 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 VisionConnectionism/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 WisconsinDecision Theory and AI: Berkeley MIT Stanford
Univ. of Michigan Univ. of WashingtonDistributed AI:
Georgia Institute Of Technology MIT Nova Southeastern University
Stanford University Univ. of Maryland Univ. of Massachusetts/Amherst
Univ. of MichiganEmotion: 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 PennsylvaniaMachine 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 WaterlooMachine 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 TorontoPhilosophy 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 SciencesPlanning:
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 CarolinaRobotics: 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 Yale Search: UCLA
Univ. of Maryland/College Park Univ. of OregonTemporal Reasoning:
Imperial College
----------------------------------------------------------------
Subject: [1-10] How can I get the email address for Joe or Jill Researcher?
The AAAI membership directory is updated annually and contains
addresses, phone numbers, and email addresses for many members of AAAI
and other AI societies. Contact info@aaai.org for information on
getting a copy of the directory (you should get a free copy if you are
a member of one of the listed societies).
See also the Email Address FAQ posting to the newsgroups soc.college
and soc.net-people.
The Artificial Intelligence and Molecular Biology Researchers database
contains names, institutions, addresses, phone, fax, email,
research interests and other related information about more than 200
researchers worldwide. The database is available via anonymous ftp from the
lhc.nlm.nih.gov:/pub/aimb-db/
There are computer- and human-readable versions available. Get the
README file for more information or send email to Larry Hunter,
<hunter@nlm.nih.gov>.
E-mail addresses for members of the Linguistics Society of America
(LSA) are available by anonymous ftp as
linguistics.archive.umich.edu:/linguistics/LSA.email.list
or by sending a message to listserv@tamvm1.tamu.edu with
"get lsa lst linguist" in the message body.
A list of "Who's Who in Fuzzy Logic" may be obtained by sending a
message to listserver@vexpert.dbai.tuwien.ac.at with
GET LISTSERVER WHOISWHOINFUZZY
in the message body. New entries and corrections should be sent to
Robert Fuller <rfuller@finabo.abo.fi>.
WHO's On-Line is a WWW biographical database of folks on the internet.
http://www.ictp.trieste.it/Canessa/ENTRIES/entries.html
For more information, contact E. Canessa <canessae@ictp.trieste.it>.
The Association for Logic Program (ALP) membership list was published
in the February 1994 issue of the newsletter (Volume 7/1). It will be
made available by anonymous ftp from Imperial College in October 1994.
----------------------------------------------------------------
Subject: [1-11] What are the rules for the game of "Life"?
Cellular Automata, of which Life is an example, were suggested by
Stanislaw Ulam in the 1940s, and first formalized by von Neumann.
Conway's "Game of Life" was popularized in Martin Gardner's
mathematical games column in the October 1970 and February 1971 issues
of Scientific American. (Shorter notes on life are alse given in the
column in each month from October 1970 to April 1971, and well as
November 1971, January 1972, and December 1972.) There's also quite a
bit on the game in "The Recursive Universe", by William Poundstone,
Oxford University Press, 1987, 252 pages.
The rules for the game of life are quite simple. The game board is a
rectangular cell array, with each cell either empty or filled. At each
tick of the clock, we generate the next generation by the following rules:
if a cell is empty, fill it if 3 of its neighbors are filled
(otherwise leave it empty) if a cell is filled, it
dies of loneliness if it has 1 or fewer neighbors
continues to live if it has 2 or 3 neighbors
dies of overcrowding if it has more than 3 neighbors
Neighbors include the cells on the diagonals. Some implementations use
a torus-based array (edges joined top-to-bottom and left-to-right) for
computing neighbors.
For example, a row of 3 filled cells will become a column of 3 filled
cells in the next generation. The R pentomino is an interestingpattern: xx
xx xTry it with other patterns of 5 cells initially occupied. If you
record the ages of cells, and map the ages to colors, you can get a
variety of beautiful images.
When implementing Life, be sure to maintain separate arrays for the
old and new generation. Updating the array in place will not work
correctly. Another optimization to to maintain a list of the cellsthat changed.
Conway has demonstrated that it is possible to construct the basic
building blocks of a computer from Life using modified glider guns.
See the last chapter of
Elwyn R. Berlekamp, John H. Conway, and Richard K. Guy, "Winning
Ways", Academic Press, New York, 1982, ISBN 0-120911-507.for details.
Some interesting patterns to use include:
*
* * ** * *
** * ** * * * *
** *** * * ****
* **** *** *** * *
* ** *
Clock Glider Block Spaceship * * *
* ****
*
Traffic Light Cheshire Cat
----------------------------------------------------------------
Subject: [1-12] What AI competitions exist?
The Loebner Prize, based on a fund of over $100,000 established by New
York businessman Hugh G. Loebner, is awarded annually for the computer
program that best emulates natural human behavior. During the
contest, a panel of independent judges attempts to determine whether
the responses on a computer terminal are being produced by a computer
or a person, along the lines of the Turing Test. The designers of the
best program each year win a cash award and a medal. If a program
passes the test in all its particulars, then the entire fund will be
paid to the program's designer and the fund abolished. For further
information about the Loebner Prize, see the URL
http://www.loebner.net/Prizef/loebner-prize.html
or write to Cambridge Center for Behavioral Studies, 11
Waterhouse Street, Cambridge, MA 02138, or call 617-491-9020.Also look at:
http://www.eecs.harvard.edu/~shieber/papers/loebner-rev-html/loebner-rev-html.ht
ml
for a published criticism of the Loebner.
The Robot World Cup Initiative (RoboCup) is an attempt to foster AI
and intelligent robotics research by providing a standard problem
where wide range of technologies can be integrated and examined. For
this purpose, RoboCup chose to use soccer game, and organize RoboCup:
The Robot World Cup Soccer Games and Conferences. In order for a robot
team to actually performa soccer game, various technologies must be
incorporated including: design principles of autonomous agents,
multi-agent collaboration, strategy acquisition, real-time reasoning,
robotics, and sensor-fusion. RoboCup is a task for a team of multiple
fast-moving robots under a dynamic environment. RoboCup also offers a
software platform for research on the software aspects of RoboCup.
Information can be found at: http://www.robocup.org/02.html
The BEAM Robot Olympics is a robot exhibition/competition started in
1991. For more information about the competition, write to BEAM Robot
Olympics, c/o: Mark W. Tilden, MFCF, University of Waterloo, Ontario,
Canada, N2L-3G1, 519-885-1211 x2454, mwtilden@watmath.uwaterloo.ca.
The Gordon Bell Prize competition recognizes outstanding achievements
in the application of parallel processing to practical scientific and
engineering problems. Entries are considered in performance,
price/performance, compiler parallelization and speedup categories,
and a total of $3,000 will be awarded. The prizes are sponsored by
Gordon Bell, a former National Science Foundation division director
who is now an independent consultant. Contestants should send a
three- or four-page executive summary to 1993 Gordon Bell Prize,
c/o Marilyn Potes, IEEE Computer Society, 10662 Los Vaqueros Cir.,
PO Box 3014, Los Alamitos, CA 90720-1264, before May 31, 1993.
AAAI has an annual robot building competition. The anonymous FTP site
for the contest is/was aeneas.mit.edu:/pub/ACS/6.270/AAAI/
This site has the manual and the rules. To be added to the
rbl-94@ai.mit.edu mailing list for discussing the AAAI robot building
contest, send mail to rbl-94-request@ai.mit.edu. See also the 6.270
robot building guide in part 4 of this FAQ.
The International Computer Chess Association presents an annual prize
for the best computer-generated annotation of a chess game. The output
should be reminiscent of that appearing in newspaper chess columns,
and will be judged on both the correctness and depth of the variations
and also on the quality of the program's written output. The deadline
is December 31, 1994. For more infor92-1071.
----------------------------------------------------------------
Subject: [1-13] Commercial AI products.
[this too must be out of date, given the life cycle of most AIsoftware...]
Commercial Expert System Shells are listed in the Expert System Shells FAQ.
See the Robotics FAQ for information on Robotics manufacturers.
Stiquito is a small (3cm H x 7cm W x 6cm L), simple (32 parts) and
inexpensive (< $30) nitinol-propelled hexapod robot developed at the
Indiana University (Bloomington) Robotics Laboratory. Its legs are
propelled by nitnol actuator wires. Each leg has one degree of freedom.
The robot walks up to 10 centimeters per minute and can carry a 9-volt
cell, a MOSIS "tiny chip" and power transistors to drive the nitinol
actuator wires. Nitinol wire (aka BioMetal, Flexinol), is a nickel-titanium
alloy which exerts useful force as it is heated by passing a current
through it. IUCS Technical Report 363a describes Stiquito's construction
and is available by anonymous ftp from
cs.indiana.edu:/pub/stiquito/ [129.79.254.191]
as are many other related files. The tech report is also
available by US mail for $5 (checks or money orders should be made payable
to "Indiana University") from Computer Science Department, Attn: TR 363a
215, Lindley Hall, Indiana University, Bloomington, Indiana 47405. A kit
containing all the materials needed to construct a simple version of
Stiquito and its controller is available for an extra $10 from the above
address (use attn line "Stiquito Kit"). To receive a video showing the
assembly of Stiquito, include an additional $10 and add "Video" to the
"Attn:" line. Anyone may build and use Stiquitos in any quantity for
educational or research purposes, but Indiana University reserves all
rights to commercial applications. Questions about Stiquito should be sent
to Prof. Jonathan W. Mills <stiquito@cs.indiana.edu>. To join the Stiquito
mailing list run by Jon Blow of UC/Berkeley, send mail to
stiquito-request@xcf.berkeley.edu.
Togai InfraLogic, Inc. (TIL) is a supplier of fuzzy logic and fuzzy
expert system software and hardware. For more information, write to
Togai InfraLogic, Inc., 5 Vanderbilt, Irvine, CA 92718, call +1 714
975 8522, fax +1 714 975 8524, or send email to info@til.com or
til!info. TIL also supports an email-server that can be reached at
fuzzy-server@til.com or til!fuzzy-server. Send an email message that
contains just the word "help" in either the subject line or the
message body for more information. A list of products can be obtained
by sending a message that contains only the line "send products.txt"
to the email-server. For an index of the contents of the server, send
a message with the line "send index".
The following is from Risks Digest 13.83 -- I have no idea what the software
does, but Colby did head up the PARRY project:
FEELING HELPLESS ABOUT DEPRESSION? Overcoming Depression 2.0 provides
computer based cognitive therapy for depression with therapeutic
dialogue in everyday language. Created by Kenneth Mark Colby, M.D.,
Professor of Psychiatry and Biobehavioural Sciences, Emeritus, UCLA.
Personal Version ($199), Professional version ($499). Malibu
Artificial Intelligence Works, 25307 Malibu Rd, CA 90265. 1-800-497-6889.
----------------------------------------------------------------
Subject: [1-14] AI Job Postings
A mailing list (AI-Jobs) once existed to help programmers and
researchers find AI programming and research positions, and to help
companies with AI programming and research positions find capable AI
programmers and researchers.
[According to the archives at ftp.cs.cmu.edu:/user/ai/jobs/, this list
has not been active since '96.]
[For neural networks, the Neuron Digest and Connectionists mailing
lists are a good source of job postings. For computer vision, the
VISION-LIST digest includes occasional job announcements. A good
source for general AI is Computists' Communique. For postdoctoral
appointments, see sci.research.postdocs.]
----------------------------------------------------------------
Subject: [1-15] Future Directions of AI
The purpose of this question is to compile a list of major ongoing and
future thrusts of AI. To be included in this list a research problem
or application must have the following characteristics:
[1] Collaborative Community Effort: It must span several subfields
of AI, requiring some degree of collaboration between AI
researchers of different specialties. The idea is to help
unify the fragmented subfields with a common purpose or
purposes.
[2] High Impact: It must address important problems of widespread interest.
Solving the problem must matter to many people and not simply
be adding another grain of sand on the anthill. This will help
motivate and excite researchers, and justify the field to outsiders.
[3] Short Horizon for Progress: It must be possible to have incremental
progress and not be an all or nothing problem. For example,
problems where we can reasonably expect to make significant
measurable progress over the next 10 years or so.
[4] Drive Basic Research: It should involve more than just
applying current technology, but should drive basic research
and the development of new technology (possibly in completely
new directions).
In short, these problems should be "Grand Challenges" for AI. If you
were trying to describe the field of AI to a layman, what concrete
problems would you use to illustrate the overall vision of the field?
Saying that the goal of AI is to produce "thinking machines that solve
problems" doesn't quite cut it.
o Knowbots/Infobots, Web Agents and Intelligent Help Desks
Unified NLU, NLG, Information Retrieval, KR, Reasoning,
Intelligent User Interfaces, Qualitative Reasoning.
o Autonomous Vehicles
Unified Robotics, Machine Vision, Machine Learning,
Intelligent Control, Planning o Machine Translation
Unified NLU, NLG, Knowledge Representation, Speech Understanding,
Speech Synthesis
It seems appropriate to mention, in this context, some of the early
goals of AI. In 1958 Newell and Simon predicted that computers would
-- by 1970 -- be capable of composing classical music, discovering
important new mathematical theorems, playing chess at grandmaster
level, and understanding and translating spoken language. Although
these predictions were overly optimistic, they did represent a set of
focused goals for the field of AI. [See H. A. Simon and A. Newell,
"Heuristic Problem Solving: The Next Advance in Operations Research",
Operation Research, pages 1-10, January-February 1958.]
----------------------------------------------------------------
Subject: [1-16] Why is this FAQ so short?
The comp.ai FAQ is in a state of flux after being inactive for a
number of years. Please send suggestions for new questions to the
maintainers for inclusion.
----------------------------------------------------------------
Subject: [1-17] Where are the FAQs for...neural nets? natural
language? artificial life? fuzzy logic? genetic algorithms?
philosophy? Lisp? Prolog? robotics?
The FAQs for various related AI fields can be found:
( this list is obviously incomplete)
comp.ai.neural-nets: ftp://ftp.sas.com/pub/neural/FAQ.html
comp.ai.nat-lang: http://www.cs.columbia.edu/~acl/nlpfaq.txtcomp.ai.alife: ?
comp.ai.fuzzy: ?comp.ai.genetic: ftp://rtfm.mit.edu/pub/usenet/comp.ai.genetic/
comp.ai.philosophy: ?comp.lang.lisp: ftp://ftp.think.com/public/think/lisp/
comp.lang.prolog: http://warbase.selwerd.cx/~dirk-jan/prolog/faq/---
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