Archive-name: ai-faq/general/part1
Posting-Frequency: monthly
Last-Modified: 1-Apr-04 rc by Ric Crabbe
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;;; ****************************************************************
;;; Answers to Questions about Artificial Intelligence *************
;;; ****************************************************************
;;; Maintained by: Amit Dubey
;;; Ric 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 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 .
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
rtfm.mit.edu:/pub/usenet/
or by sending a mail message to mail-server@rtfm.mit.edu 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 llv@linuxmail.org (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
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
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