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AIList Digest Volume 6 Issue 083
AIList Digest Friday, 29 Apr 1988 Volume 6 : Issue 83
Today's Topics:
Education - AI Texts & Lisp Machine Mailing List,
AI Tools - Boyer and Moore's Prover,
Opinion - Exciting Work in AI & Expert Systems for Graphic Design,
History - Demons and Other AI Constructs
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Date: 25 Apr 88 04:57:12 GMT
From: beowulf!demers@sdcsvax.ucsd.edu (David E Demers)
Subject: Re: AI texts
In article <1516@gumby.cs.wisc.edu> g-zeiden@gumby.cs.wisc.edu
(Matthew Zeidenberg) writes:
>I'm teaching intro AI here at the Univ. of Wisconin this coming
>summer, and I'm trying to choose a text. I'm considering Rich,
>Winston, Nilsson and Tanimoto's books. Any opinions?
>
For an intro course, the above are all good; also, Charniak &
McDermott. I suppose it depends on whether you want a broad
overview, and on what YOU think AI really is. Some LISP should
be a prerequisite, PROLOG may be helpful. And of course the new
hot topic is connectionism/neural networks. (I am still a grad
student, & speak from the point of view of having studied rather
than taught...)
Dave DeMers UCSD
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Date: 26 Apr 88 05:23:50 GMT
From: portal!cup.portal.com!tony_mak_makonnen@uunet.uu.net
Subject: Re: AI texts
I'm teaching intro AI here at the Univ. of Wisconin this coming
summer, and I'm trying to choose a text. I'm considering Rich,
Winston, Nilsson and Tanimoto's books. Any opinions?
Thanks in advance.
I would recommend you take a look at Parsaye and chignel's book
"Expert Systems for Experts" , John Wiley & Son. It is more
oriented to shells and less to Lisp.
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Date: 25 Apr 88 21:30:00 GMT
From: ong@p.cs.uiuc.edu
Subject: Re: AI texts
How about Nilsson and Genesereth's Logical Foundations of AI? Some of
the chapters are definitely not introductory stuff, but it is written
in a very clear and concise manner.
Students might find it interesting to be exposed to neural networks in
an introductory AI course, too.
------------------------------
Date: 27 Apr 88 18:11:22 GMT
From: rochester!daemon@bbn.com (Brad Miller)
Subject: Re: Lisp Machines mailing list sought
Date: 25 Apr 88 20:44:14 GMT
From: mendozag@pur-ee.UUCP (Grado)
Are there any mailing lists concerned with the Symbolics
Lisp Machines?
I remember I read about one in the Arpanet some time ago.
Yes: SLUG@ai.sri.com; sign up via SLUG-Request@ai.sri.com
(SLUG stands for Symbolics Lisp Users Group)
----
Brad Miller U. Rochester Comp Sci Dept.
miller@cs.rochester.edu {...allegra!rochester!miller}
------------------------------
Date: Fri, 22 Apr 88 19:21:13 CDT
From: Robert S. Boyer <boyer@CLI.COM>
Subject: Availability of Boyer and Moore's Prover
A Common Lisp version of our theorem-prover is now available under the
usual conditions: no license, no copyright, no fee, no support. The
system runs well in three Common Lisps: KCL, Symbolics, and Lucid.
There are no operating system or dialect conditionals, so the code may
well run in other implementations of Common Lisp.
Included as sample input is the work of Hunt on the FM8501
microprocessor and of Shankar on Goedel's incompleteness theorem and
the Church-Rosser theorem.
To get a copy follow these instructions:
1. ftp to Arpanet/Internet host cli.com.
(cli.com currently has Internet numbers
10.8.0.62 and 192.31.85.1)
2. log in as ftp, password guest
3. get the file /pub/nqthm/README
4. read the file README and follow the directions it gives.
Inquiries concerning tapes may be sent to:
Computational Logic, Inc., Suite 290
1717 W. 6th St.
Austin, Texas 78703
A comprehensive manual is available. For information on obtaining a
copy, write to the address above.
Bob Boyer J Moore
boyer@cli.com moore@cli.com
Due to major changes in the Arpanet, getting through to cli.com may be
difficult starting May 1 until one of the alternative Internet options
is solidly in place.
------------------------------
Date: 24 Apr 1988 18:52 (Sunday)
From: munnari!nswitgould.oz.au!wray@uunet.UU.NET (Wray Buntine)
Subject: Re: Exciting work in AI
Ehud Reiter (V6#69) was eliciting the following (summarised by Spencer Star)
> Exiting work in AI. The three criteria are:
> 1. Highly thought of by at least 50% in the field.
> 2. Positive contribution
> 3. Real AI
Spencer Star made a number of suggestions of "exiting" work.
I disagree on some of them. I mention only 1 below.
> Another area involves classification trees of the sort generated by
> Quinlan's ID3 program.
Ross's original ID3 work (and the stuff usually reported in Machine Learning
overviews) and much subsequent work by him and others (e.g. pruning)
actually fails the "real AI" test. It was independently developed by
a group of applied statisticians in the 70's and is well known
Breiman, L., Friedman, J.H., Olshen, R.A. and Stone, C,J. (1984)
"Classification and Regression Trees", Wadsworth
Ross's more recent work does significantly improve on Breiman et al.s stuff.
To my knowledge, however, it is not yet widely known. Try looking in IJCAI-87.
His latest program is actually called C4 (heard of it?), has been for years,
and I think it is closer to real AI (e.g. concern for comprehensibility),
though it still has an applied statistics flavour. Perhaps this fails the
"highly thought of by 50%" test. Another year maybe.
--------------
Wray Buntine
wray@nswitgould.oz
University of Technology, Sydney
------------------------------
Date: 26 Apr 88 09:45:09 GMT
From: mcvax!ukc!strath-cs!glasgow!gilbert@uunet.uu.net (Gilbert
Cockton)
Subject: Re: expert systems for graphic design?
In article <10494@sunybcs.UUCP> dmark@sunybcs.UUCP (David Mark) writes:
>REQUEST: Does anyone our there know of any expert systems (or other
>kinds of software systems) for evaluating the graphic design of a
>display?
>If people reply to me via email, I will summarize responses to the net.
Sorry, email can be flakey from Europe.
Main references I know are
%A D.J. Streveler
%A A.I. Wasserman
%T Quantitative Measures of the Spatial Properties of Screen Designs
%J INTERACT'84
%V 1
%I Elsevier/IEE
%P 124-133 (participants edition)
%D 1984
%O 1985 edition pub. North Holland
%A T. Tullis
%T Designing a menu-based interface to an operating system
%J CHI '85
%P 79-84
%D 1985
%A T.S. Tullis
%T Optimising the Usability of Computer-Generated Displays
%B People and Computers: Designing for Usability
%E M.D. Harrison and A. Monk
%I Cambridge University Press
%C Cambridge
%P 604-613
%D 1986
These measures covered are useful, but very crude. Graphic designers
are not ones for writing things down, nor can I see them rushing to
have their knowledge elicited by production rule hackers. It is far
more efficient to find a NUMBER of graphic designers locally and ask
them to evaluate your display layouts. Then use your judgement to
decide on which advice to take.
If you've got a long time, you could implement some alternative
designs for aspects of the display and get a human factors expert (not
a theoretical psychologist) to help you to compare human performance
effects of the design alternatives. You could also discuss design
alternatives in the first place with such an expert. Use paper here,
as it's more efficient in early design than most screen generators -
MacDraw is a good early prototyping tool.
Finally, try things out on representative end-users, who may like
neither the aesthetics of your preferred designer nor the optimum
performance of the human factors experiment.
This all seems harder than just running a program, but I can assure
you that it is all a lot easier than trying to design one to do an
equivalent job. We do not have a computational account of good
graphical design, are unlikely to gain one in the near future, and
probably never will reduce aesthetics to some thing as ugly as a
Turing equivalent formalism. Of course, the phenomenological aspects
of end-user preferences can NEVER be automated or simulated, because
such phenomenology is defined to be human experiences and
categorically absolutely nothing else. It is as computable as a daffodil!
------------------------------
Date: 27 Apr 88 18:11:46 EDT
From: John Sowa <SOWA@ibm.com>
Subject: Historical remarks on demons and other AI constructs
In response to some recent questions, I thought that it might be useful
to cite a few historical references:
1. The first use of the term demon in AI was for the system Pandemonium
by Oliver Selfridge (1958). He developed it as a system for
learning to recognize human-keyed Morse code. It consisted of
low-level demons that looked for patterns. When a demon found
its pattern, it would "shout". Higher-level demons listened for
shouts from lower-level demons. They, in turn, would shout when
they heard a characteristic pattern of shouts.
2. The term "demon" was introduced into physics by Maxwell, who
used it in thought experiments in thermodynamics; e.g. imagine
a demon who watched molecules bouncing around and opened a trap
door to allow only the fast ones to pass through. In principle,
it could reduce entropy by separating hot gas from cool gas.
However, the entropy of the demon itself would increase. For
a discussion of demons in physics, see von Neumann (1951), who
contributed to physics as well as logic, set theory, and even
computers.
3. While we're mentioning von Neumann, I have heard some people
distinguish highly parallel computers from "von Neumann machines."
However, von Neumann (1958) wrote one of the first books about
parallel computation and the possibility of simulating the brain.
So the term "von Neumann machine" could refer either to conventional,
single-CPU machines or to highly parallel connectionist machines.
4. A previous note mentioned Carl Hewitt's PLANNER as a source for the
three-way distinction between if-needed, if-added, and if-deleted
demons. The MIT reports may not be easy to find, but there is a
paper by Hewitt (1969) in the first IJCAI. That paper is confusing
and hard to read, but you can find the three-way distinction in it.
Although Hewitt did not invent if-needed or if-added demons, I do
not know of any earlier version of an if-deleted demon.
5. Goal-directed or if-needed patterns were well developed in the
General Problem Solver. The most definitive reference to GPS is
the book by Ernst & Newell (1969), but there are papers on early
versions dating back to 1959.
6. The 1969 version of GPS also had a well developed use of "schemas,"
which were frame-like structures that predated frames by at least
6 or 7 years. A schema always had unbound variables. When all
its variables were instantiated, it was called a "model."
7. The term schema was introduced to Newell & Simon by Adriaan de Groot,
who visited Carnegie in the 1960s. De Groot (1965) wrote a highly
influential book on thinking processes in chess, in which he applied
the theories of the psychologist Otto Selz (1913, 1922). Selz had
a theory of "schematic anticipation" in which a schema served as a
goal towards which the thinking processes were directed. Selz even
described backtracking search procedures as a way of satisfying
the goal and used a network notation for his schemas. Quillian,
who studied with Newell & Simon, cited Selz in his thesis (1966),
but the abridged version reprinted in Minsky (1968) doesn't
mention Selz.
John Sowa
References:
O. G. Selfridge (1968) "Pandemonium: a paradigm for learning," in
Mechanisation of Thought Processes, Proceedings of a symposium held
at the National Physical Laboratories, Nov. 1958, Her Majesty's
Stationery Office, London, pp. 511-531.
J. von Neumann (1951) Mathematical Foundations of Quantum Mechanics,
Princeton University Press, Princeton, NJ.
J. von Neumann (1958) The Computer and the Brain, Yale University Press,
New Haven.
C. Hewitt (1969) "PLANNER: a language for proving theorems in robots,"
Proceedings of IJCAI, pp. 295-301.
G. W. Ernst & A. Newell (1969) GPS: A Case Study in Generality and
Problem Solving, Academic Press, New York.
A. de Groot (1965) Thought and Choice in Chess, Mouton, The Hague.
O. Selz (1913) Ueber die Gesetze des geordneten Denkverlaufs,
Spemann, Stuttgart.
O. Selz (1922) Zur Psychologie des produktiven Denkens und des Irrtums,
Friedrich Cohen, Bonn.
M. R. Quillian (1966) Semantic Memory, Report AD-641671, Clearinghouse
for Federal Scientific and Technical Information.
M. Minsky (1968) Semantic Information Processing, MIT Press, Cambridge, MA.
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End of AIList Digest
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