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	(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.

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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.

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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

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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 crwfrdDESPAM@umich.edu:] 

"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."


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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.

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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/

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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, 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.

 ---

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
, ICCA President, Computing Science Department,
University of Alberta, Edmonton, Canada T6G 2H1, call 403-492-3971, or
fax 403-492-1071.

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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.

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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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