Artificial Intelligence FAQ:1/6 General Questions & Answers [Monthly posting]
From: [email protected] (Ric Crabbe and Amit Dubey)
Newsgroups: comp.ai
Subject: Artificial Intelligence FAQ:1/6 General Questions & Answers [Monthly posting]
Date: Wed, 5 May 2004 19:13:57 +0000 (UTC)
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Summary: Frequently asked questions about AI
Archive-name: ai-faq/general/part1
Posting-Frequency: monthly
Last-Modified: 1-Apr-04 rc by Ric Crabbe
Version: 2.1
Maintainer: Ric Crabbe <[email protected]> and Amit Dubey <[email protected]>
URL: http://www.faqs.org/faqs/ai-faq/general
Size: 46550 bytes, 1051 lines
;;; ****************************************************************
;;; Answers to Questions about Artificial Intelligence *************
;;; ****************************************************************
;;; Maintained by: Amit Dubey <[email protected]>
;;; Ric Crabbe <[email protected]>
;;; <http://www.cs.usna.edu/~crabbe>
;;; 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) 1999,2000-04 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.
*** What's new?
;;; 01-Apr-04 rc Replaced "game of life" question with
information theory. Other assorted fixes.
;;; 29-Jun-03 rc Have begun a section on comercial AI software.
Added question on "tell me all about AI"
;;; 29-May-03 rc Added question on A*
*** Topics Covered:
Part 1:
[1-0] What is the purpose of this newsgroup?
[1-1] I have a Question not covered in the FAQ...
[1-2] What is AI?
[1-3] What's the difference between strong AI and weak AI?
[1-4] I have little/no background in CompSci/AI, can you tell
me in detail how AI works?
[1-5] I'm a programmer interested in AI. Where do I start?
[1-6] What's an agent?
[1-7] History of AI.
[1-8] What has AI accomplished?
[1-9] What are the branches of AI?
[1-10] What are good programming languages for AI?
[1-11] What's the difference between "classical" AI and "statistical" AI?
[1-12] I have the idea for an AI Project that will solve all of AI...
[1-13] Glossary of AI terms.
[1-14] In A*, why does the heuristic have to always underestimate?
[1-15] I'm considering studying AI. What information is there for me?
[1-16] What are good graduate schools for AI?
[1-17] No really, just give me a ranking of the best
graduate schools for AI!
[1-18] What are the ratings of the various AI journals?
[1-19] Where can I find conference information?
[1-20] How can I get the email address for Joe or Jill Researcher?
[1-21] What does it mean to say a game is 'solved'? Is tic-tac-toe
solved? How about X?
[1-22] What's this Information Theory thing?
[1-23] What AI competitions exist?
[1-24] Open source software and AI.
[1-25] AI Job Postings
[1-26] Future Directions of AI
[1-27] Where are the FAQs for...neural nets? natural language?
artificial life? fuzzy logic? genetic algorithms?
philosophy? Lisp? Prolog? robotics?
Part 2 (AI-related News, Newsgroups and Mailing Lists):
- List of all known AI-related newsgroups, newsgroup archives, mailing
lists, and electronic bulletin board systems.
http://www.faqs.org/faqs/ai-faq/general/part2/preamble.html
Part 3 (AI-related Associations and Journals):
- List of AI-related associations and journals, organized by subfield.
http://www.faqs.org/faqs/ai-faq/general/part3/preamble.html
Part 4 (Bibliography):
- Bibliography of introductory texts, overviews and references
- Addresses and phone numbers for major AI publishers
- Finding conference proceedings
- Finding PhD dissertations
http://www.faqs.org/faqs/ai-faq/general/part4/preamble.html
Part 5 (FTP and WWW Resources and Repositories):
- Information on Web resources and software repositories for AI.
- Information on Technical Papers in AI
- Web journals
- Part 5 concentrates mostly on documents and collections of links
to other AI resources
http://www.faqs.org/faqs/ai-faq/general/part5/preamble.html
Part 6 (AI Open-Source Software by Sub-field)
- An A-Z (well A-T anyway) of Open source (or at least free)
software with relation to AI.
- A nascent list of commercial AI software,
http://www.faqs.org/faqs/ai-faq/general/part6/preamble.html
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 NO-LONGER available via anonymous
FTP from:
ftp://ftp.cs.ucla.edu/pub/AI/
as the files ai_[1-7].faq.
The cannonical source is now:
http://www.faqs.org/faqs/ai-faq/general
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 [email protected]
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 ever 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
ftp://rtfm.mit.edu/pub/usenet/
or by sending a mail message to [email protected] with subject
"help". http://www.faqs.org/ has a nice webified version.
Subject: [1-1] I have a Question not covered in the FAQ...
This FAQ tries to answer many introductory issues in Artificial
Intelligence, but there are many questions it cannot or does not
answer. While the FAQ maintainers welcome email about the FAQ and AI
in general, the proper place to ask AI questions is the comp.ai
newsgroup itself - that's what it's for. As a practical issue, the
maintainers reply to FAQ related mail on a monthly basis, so replies
to questions are likely to be delayed.
Subject: [1-2] What is AI?
Artificial intelligence ("AI") can mean many things to many people.
Much confusion arises because 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: [1-3] 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 and possibly even be conscious of
themselves. 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: [1-4] I have little/no background in CompSci/AI, can you tell
me in detail how AI works?
No. AI is a scientific and engineering discipline depending on
sophisticated Computer Science techniqes, mathematics, etc. It also
is sub-divided into many distinct subfields. At the International
Joint Conference on Artificial Intelligence in 2003, the program
committee divided the papers into nearly forty different topic areas.
It is not really practical to expect to understand the technical
details of AI from a USENET forum.
On the other hand, it is possible to get the general gist of the field
from several books. If you have a computer science background, you
should investigate one of the texts listed in question [4-0]. If you
don't, then you may be interested in Raymond Kurzweil's "The Age of
Intelligent Machines".
Subject: [1-5] 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]. Also, check out the web links in section [5-2].
[1-5a] 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-based
systems. Often, commercial games tend to use finite state machines
for computer players. Recently, discrete Markov models have been used
to simulate unpredictible human players (the buzzword compliant name
being "fuzzy" finite state machines).
A recent popular game, "Black and White", used machine learning
techniques for the non-human controlled characters. Basic
reinforcement learning, perceptrons and decision trees were all
parts of the learning system. Is this the begining of academic AI
in video games?
Subject: [1-6] What's an agent?
A very misused term. Today, an agent seems to mean a stand-alone
piece of AI-ish software that scours across the internet doing
something "intelligent." Russell and Norvig define it as "anything
that can can be viewed a perceiving its environment through sensors
and acting upon that environment through effectors." Several papers
I've read treat it as 'any program that operates on behalf of a
human,' similar to its use in the phrase 'travel agent'. Marvin
Minsky has yet another definition in the book "Society of Mind."
Minsky's hypothesis is that a large number of seemingly-mindless
agents can work together in a society to create an intelligent society
of mind. Minsky theorizes that not only will this be the basis of
computer intelligence, but it is also an explaination of how human
intelligence works. Andrew Moore at Carnegie Mellon University once
remarked that "The only proper use of the word 'agent' is when
preceded by the words 'travel', 'secret', or 'double'."
Subject: [1-7] History of AI.
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.
The introductory material in:
Russell, S and Norvig, P, "Artificial Intelligence: A Modern
Approach", Prentice Hall, 1995
is also quite good.
Subject: [1-8] 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
- Financial software, which is used by banks to scan credit card
transactions for unusual patterns that might signal fraud. One piece
of software is estimated to save banks $500 million annually.
- Applications of expert systems/case-based reasoning: a computerized Leukemia
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
- Physical design analysis programs,such as for buildings and highways.
- Fuzzy controllers in dishwashers, etc.
Here is a cute A-Z list made by [email protected] (Lauren Vincent):
AnswerBus (http://www.answerbus.com/)
Babel Fish (http://babel.altavista.com/)
Connexor (http://www.connexor.com/)
Deep Blue (http://www.research.ibm.com/deepblue/)
Emdros (http://emdros.org/)
Flip Dog (http://flipdog.monster.com/)
Gigablast (http://www.gigablast.com/)
Hermit Crab (http://www.sil.org/computing/hermitcrab/)
InDiGen (http://www.coli.uni-sb.de/cl/projects/indigen.html)
Jack the Ripper (http://www.triumphpc.com/jack-the-ripper/)
KartOO (http://www.kartoo.com/)
Loebner Prize (http://www.loebner.net/Prizef/loebner-prize.html)
Mamma (http://www.mamma.com/)
NEGRA (http://www.coli.uni-sb.de/sfb378/2002-2004/projects.phtml?action=2&w=2&l=en)
OpenFind (http://www.openfind.com/en.web.php)
PolyWorld (http://homepage.mac.com/larryy/larryy/PolyWorld.html)
Questia (http://www.questia.com/)
RiniNet (http://sourceforge.net/projects/rininnlib/)
SIGS (http://www.acm.org/sigs/)
Turing Test (http://cogsci.ucsd.edu/~asaygin/tt/ttest.html)
Useroo (http://useroo.businessresearchsources.com/)
Vivisimo (http://www.vivisimo.com/)
WordNet (http://www.cogsci.princeton.edu/~wn/)
Xconq (http://sources.redhat.com/xconq/)
YY (http://www.yy.com/)
Zabaware (http://www.zabaware.com/)
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
like Engineering. 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, despite minimax search
being one of the great early successes of AI. Nowadays, people are
still studying all sorts of things that are currently considered the
prerequisites of intelligence, such as intuition and emotion, but you
can bet that if and when they solve some part, some will say "oh,
that's just Engineering..."
ref:
Alan M. Turing. Computing machinery and intelligence. Mind,
LIX(236):433-460, October 1950. (http://www.abelard.org/turpap/turpap.htm)
Sheiber, S, "Lessons from a Restricted Turing Test". Communications of
the Association for Computing Machinery, volume 37, number 6, pages
70-78, 1994
Subject: [1-9] 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. (Part of the "machine learning"
problem).
Constraint Statisfaction - solving NP-complete problems, using a
variety of techniques.
Knowledge Engineering/Representation - turning what we know about
a particular domain into a form in which a computer can
understand it.
Machine Learning - Programs that learn from experience or data.
Natural Language Processing(NLP) - Processing and (perhaps)
understanding human ("natural") language. Also known as
computational linguistics.
Neural Networks(NN) - The study of programs that function in a
manner similar to how animal brains do.
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
Robotics - The intersection of AI and robotics, this field tries
to get (usually mobile) robots to act intelligently.
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...
Visual Pattern Recognition - The ability to reproduce the
human sense of sight on a machine.
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: [1-10] What are good programming languages for AI?
This topic can be somewhat sensitive, so I'll probably tread on 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/C++, recently
Java, and even more recently, Python.
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. Read Paul Graham's essay, "Beating the Averages"
for a discussion of some serious advantages:
http://www.paulgraham.com/avg.html
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 traditional 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 that it's hard to learn.
C/C++- The speed demon of the bunch, C/C++ is mostly used when the
program is simple, and excecution speed is the most important.
Statistical AI techniques such as neural networks are common examples
of this. Backpropagation is only a couple of pages of C/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.
Python- This language does not have widespread acceptance yet, but
several people have suggested to me that it might end up passing Java
soon. Apparently the new edition of the Russell-Norvig textbook will
include Python source as well as Lisp. According to Peter Norvig,
"Python can be seen as either a practical (better libraries) version
of Scheme, or as a cleaned-up (no $@&%) version of Perl." For more
information, especially on how Python compares to Lisp, go to
http://norvig.com/python-lisp.html
Also see section [6-1] for implementations of new languages that might
be pertainant to AI practitioners and researchers.
(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: [1-11] What's the difference between "classical" AI and
"statistical" AI?
Statistical AI, arising from machine learning, tends to be more
concerned with "inductive" thought: given a set of patterns, induce
the trend. Classical AI, on the other hand, is more concerned with
"deductive" thought: given a set of constraints, deduce a conclusion.
Another difference, as mentioned in the previous question, is that C++
tends to be a favourite language for statistical AI while LISP
dominates in classical AI.
A system can't be truly intelligent without displaying properties of
both inductive and deductive thought. This lends many to beleive that
in the end, there will be some kind of synthesis of statistical and
classical AI.
Subject: [1-12] I have the idea for an AI Project that will solve all
of AI...
Great! Welcome to the club and tell us all about it. Most poeple in
the community genuinely want new people to be thinking about AI. You
should be aware that you will probably not get a whole lot of
enthusiasm from the established scientists for a few reasons:
- We receive or hear about such proposals about once a month. The
vast majority are naive.
- Many smart people have been thinking about the AI problem for a long
time. There have been many ideas that have been pursued by
sophisticated research teams which turned out to be dead ends. This
includes all of the obvious ideas. Most grand solutions proposed
have been seen before (about 70% seem to be recapitulations of
Minsky proposals).
- The grand ideas are almost always far too vague to implement. One
of the tough lessons of graduate school is how to turn a vague idea
into something that is implementable and testable. Unless you have
experience at it, it is unlikely your first try will have the needed
precision.
- It is the general opinion of the research community that we're just
not ready to solve the general AI problem yet (cf. question on
CYC). Why that is should be addressed in another question.
OK, now that we've covered the harsh reality, you shouldn't get
discouraged. If you're having fun with it, keep doing it. You're
guaranteed to learn something while participating in a fascinating
hobby. Who knows- you may still come up with a really great and new
idea. Finally, [and this is just Ric's opinion] most of the really
interesting AI people started out because they had the same kind of
idea to make AI better than it is now.
Subject: [1-13] 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:
1. 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.) 2. An ancient canaanite city that was
occupied by the Israelites and is mentioned in the bible as
well as in other ancient texts. (thanks to Omri Safren)
Alpha-Beta Pruning:
A method of limiting search in the MiniMax algorithm. The
coolest thing you learn in an undergraduate course. If done
optimally, it reduces the branching factor from B to the
square root of B.
Animat Approach:
The design and study of simulated animals or adaptive real robots
inspired by animals. (From www-poleia.lip6.fr/ANIMATLAB - click on
"English page")
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.
Computational Linguistics:
The branch of AI that deals with understanding human language. Also
called natural language processing.
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.
Embodiment:
An approach to Artificial Intelligence that maintains that the
only way to create general intelligence is to use programs
with 'bodies' in the real world (i.e. robots). It is an
extreme form of Situatedness, first and most strongly put
forth by Rod Brooks at MIT.
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.
Generate and Test:
The basic model for performing search in any search space.
"The purest form of `generate and test' is: 1. generate all
the possible [options] that I would even remotely consider
taking next, 2. test each [option] in the generated set to
filter out bad ones, and possibly to prioritize the rest. How
much you move away from this "pure" form depends on how much
of the testing you try to move into the generation stage.
What we often strive for in intelligent systems is:
1. generate only the most appropriate action 2. no testing is
needed But what we usually end up with is: 1. generate only
the best candidates (moving some of the testing conditions
into the generator), 2. perform a more strenuous test on the
small set of generated actions, for a final selection"
-Randolph_M._Jones <[email protected]>
Heuristic:
The dictionary defines it as a method that serves as an aid to
problem solving. It is sometimes defined as any 'rule of
thumb'. Technically, a heuristic is a function that takes a
state as input and outputs a value for that state- often as a
guess of how far away that state is from the goal state. See
also: Admissibility, Search.
Information Extraction:
Getting computer-understandable information from human-readable
(ie natural language) documents.
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*.
Language Acquisition:
A relatively new sub-branch of AI; traditionally computational
linguists tried to make computers understand human language by
giving the computer grammar rules. Language acquisition is a
technique for the computer to generate the grammar rules itself.
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.
Natural Language (NL):
Evolved languages that humans use to communicate with one another.
Natural Language Queries:
Using human language to get information from a database.
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'.
Situatedness:
The property of an AI program being located in an environment
that it senses. Via its actions, the program can select its
sensation input, as well as change its environment.
Situatedness is often considered necessary in the Animat
approach. Some researchers claim that situatedness is key to
understanding general intelligence. (see Embodiment)
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: [1-14] In A*, why does the heuristic have to always
underestimate?
Recall that in A*, a number is assigned to each node, its f-cost.
f-cost is defined as f(n)=g(n)+h(n), where g(n) is the cost of
traveling to node n, and h(n) is the heuristic guess at traveling from
node n to the goal. A* expands nodes based on minimal f-cost (i.e. it
looks at all the nodes it knows about but hasn't yet examined closely,
and picks the one with the smallest f(n)). Let's look at the
following situation:
+-+
|n|
+-+
/ \
+-+ +-+
|o| |p|
+-+ +-+
/ \
+-+ +-+
|g|---------|q|
+-+ +-+
n is an already expanded node, and A* is trying to decide if it wants
to expand o or p. If g is the goal node, then o is on the shorter
path, so we want A* to pick o. Lets assume that g(n) = 5 and the cost
between nodes is always 1. Therefore g(o)=6 and g(p)=6. Now lets
assume that our heuristic sometimes overestimates, so that h(o)=5,
h(p)=3 and h(q)=2.
In this case, f(o)=g(o)+h(o) = 6+5=11
f(p)=g(p)+h(p) = 6+3=9,
so A* would expand p next, discovering node q. Then it decides which
node to expand, f(o)=g(o)+h(o) = 6+5=11
f(q)=g(q)+h(q) = 7+2=9,
so A* would expand q next, discovering g. Then it decides which node
to expand, f(o)=g(o)+h(o) = 6+5=11
f(g)=g(g)+h(g) = 8+0=8,
So A* would discover node g, notice that it is a goal and return the
path n->p->q->g, which is _not_ the shortest path.
The intuition here is that the overestimate of h(o) led A* to look at
another path where the overestimate was less bad.
Subject: [1-15] I'm a student considering further study AI. What information
is there for me?
Aaron Sloman has written an essay addressing this question, aimed at people
who know little about it. Please see
http://www.cs.bham.ac.uk/~axs/misc/aiforschools.html
Subject: [1-16] What are best graduate schools for AI?
The short answer is: MIT, CMU, and Stanford are historically the
powerhouses of AI and still are the top 3 today.
There are however, hundreds of schools all over the world with at
least one or two active researchers doing interesting work in AI.
What is most important in graduate school is finding an advisor who is
doing something YOU are interested in. Read about what's going on in
the field and then identify the the people in the field that are doing
that research you find most interesting. If a professor and his
students are publishing frequently, then that should be a place to
consider.
Subject: [1-17] No really, just give me a ranking of the best
graduate schools for AI!
[stolen from Randy Crawford [email protected]:]
"A single number that assesses a CS department's worth across the
spectrum of AI topics has increasingly less meaning as you descend the
rankings. Few schools can claim to have outstanding profs that
represent all areas in AI. Perhaps no schools can. As you descend
through school reputations from CMU/MIT/Stanford/UC Berkeley down, you
lose breadth as much as excellence. It then becomes increasingly
important that you clearly define the subtopic in AI that you want
ranked... What's more, reputations can change quickly. Ten years ago
Yale was among the best in AI, and five years ago, Chicago was quite
strong. Today, not."
Subject: [1-18] What are the ratings of the various AI journals?
ISI (Institute for Scientific Information NOT Information Sciences
Institute) produces an annual database called the Journal Impact
Factors. Check your library. Lee Giles has done a nice job of
extracting this information for some AI journals. See:
http://www.neci.nj.nec.com/homepages/giles/Citation.index/
Thanks to Bob Fisher and Dean Hougen for this answer.
You might also want to look at CiteSeer "Earth's largest free
full-text index of scientific literature." It also tracks citations.
For a somewhat complete list of AI journals listed by area, see part 3
of this FAQ.
Subject: [1-19] Where can I find conference information?
Georg Thimm maintains a webpage that lets you search for upcoming
or past conferences in a variety of AI disciplines. Check out:
http://www.drc.ntu.edu.sg/users/mgeorg/enter.epl
Subject: [1-20] How can I get the email address for Joe or Jill
Researcher?
This question is an anachronism. The correct way to get someone's
email address is to Google them. If that fails, try posting to the
comp.ai newsgroup.
Subject: [1-21] What does it mean to say a 2-player game is 'solved'?
Is tic-tac-toe solved? How about game z?
We say a game is solved when we know for sure the result when both
players play optimally. The result is either a guaranteed win for the
first player, a guaranteed win for the second player, or a draw. We
find this out by searching the mini-max game tree to the game ending
positions. If you do this for 3x3 tic-tac-toe, it is easy to see that it
is a forced draw.
other games:
3x3x3 tic-tac-toe: win for the first player.
4x4x4 tic-tac-toe: win for the first player.
Connect-4: win for the first player.
Go-Moku: win for the first player.
[Maintainer's note: Please let us know about your favorite solved
game.]
Subject: [1-22] What's this Information Theory thing?
Information Theory was developed to describe properties of
communication across networks. It turns out that it has all sorts of
applcations in AI and machine learning as well. A good tutorial can
be found at:
http://www-2.cs.cmu.edu/~dst/Tutorials/Info-Theory/
Subject: [1-23] 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.html
for a published criticism of the Loebner.
Hugh G. Loebner has written a reply to Prof. Shieber's critique. It
may be found at:
http://loebner.net/Prizef/In-response.html
---
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, [email protected].
---
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
[email protected] mailing list for discussing the AAAI robot building
contest, send mail to [email protected]. See also the 6.270
robot building guide in part 4 of this FAQ.
---
CASC theorem prover competition is held annually at the CADE
conference. First-order logic theorem prover compete for recognition
and plaques. The web page for this years contest (1999) is found at:
http://www.cs.jcu.edu.au/~tptp/CASC-16/
---
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 information, write to Tony Marsland
<[email protected]>, ICCA President, Computing Science Department,
University of Alberta, Edmonton, Canada T6G 2H1, call 403-492-3971, or
fax 403-492-1071.
Subject: [1-24] Open Source Software and AI
Some of the more interesting AI programs end up getting released to the
Web, usually with liscence granting redistrubition for non-commercial
purposes, or, increasingly, under the GNU Public License. See Part 6 of the
FAQ for more information.
Subject: [1-25] AI Job Postings
Computists International publishes a list of AI related jobs, which is
also posted periodically to comp.ai at the request of the moderator.
Computists International also publishes a set of informative newsletters
that may be subscribed to at http://www.computists.com with membership.
Student fees are (as of 3/30/00) $22.50 and professional fees are $47.50.
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.
A new list (as of 16 Jan 2003) is available at
http://groups.yahoo.com/group/Artificial_Intelligence_Jobs/
Subject: [1-26] Future Directions of AI
[Note:as of 2002, this is out of date.]
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: [1-27] 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.txt
comp.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/
In general, http://www.faqs.org/ is a good place to check for the
latest FAQs in most areas.
---
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