Copy Link
Add to Bookmark
Report
AIList Digest Volume 5 Issue 188
AIList Digest Monday, 27 Jul 1987 Volume 5 : Issue 188
Today's Topics:
Queries - Graphics/AI Bibliography &
Blackboard Architectures in Prolog &
VPExpert Parameters &
Knowledge Representation in Sanskrit,
Techniques - Garbage Collection Suppression,
Philosophy - Natural Kinds & AI, Science, and Pseudo-Science
----------------------------------------------------------------------
Date: 22-JUL-1987 15:13:29
From: THOWARD%graphics.computer-science.manchester.ac.uk@Cs.Ucl.AC.UK
Subject: Graphics-AI bibliography
I am currently investigating what work has been done on connecting/integrating
AI methods and computer graphics. I would be very grateful if anyone can
send me any references, or bibliographies (or comments!) etc in this area.
If there's enough interest, I will summarise responses. Thanks...
______________________________________________________________________________
- Toby Howard -
Computer Graphics Unit, Department of Computer Science
Manchester University, England, M13 9PL. Phone: 061 273 7121 x5429/5406
Janet: thoward@uk.ac.man.cs.cgu
ARPA: thoward%cgu.cs.man.ac.uk@cs.ucl.ac.uk
------------------------------
Date: Wed 22 Jul 87 11:52:28-CDT
From: OLIVIER J. WINGHART <CS.WINGHART@R20.UTEXAS.EDU>
Subject: Blackboard architectures in Prolog
I am looking for natural ways of implementing a blackboard architecture
in Prolog. Has anyone already thought about this, and are there any papers
that I could look at ? I would appreciate any pointer.
Olivier
cs.winghart@utexas.edu
------------------------------
Date: Fri, 24 Jul 87 18:00:05 EDT
From: Brady@UDEL.EDU
Subject: VPExpert Parameters
The VPExpert manual says that data can be passed to a batch
file, and that this is the only way to directly pass parameters
to an external program. But when I try to do this, the system
tells me the syntax of my call is wrong. I am sure my error is
not in the call to the batch file itself, since I am able to call
and execute a batch file that does not require parameters.
Anyone out there using this shell who has figured
out how to pass parameters to a batch file, please send me
mail. I will post answers back to the net. Thank you.
/////////
joe brady
------------------------------
Date: Thu, 23 Jul 87 10:54:47 PDT
From: bwidlans%zodiac@ads.arpa (Bob Widlansky)
Subject: Knowledge Representation in Sanskrit
Recently, I read a short intriguing article in AI magazine about the
First International Conference on Knowledge Representation and
Inference in Sanskrit (held in Bangalore, India between December
20-22, 1986).
Does anyone know where I can get a copy of the proceedings?
If you do, please contact me at bwidlans@ads.ARPA
Thank you,
Bob Widlansky
------------------------------
Date: 22 Jul 87 14:28:51 GMT
From: "J. A. \"Biep\" Durieux" <mcvax!cs.vu.nl!biep@seismo.CSS.GOV>
Reply-to: "J. A. \"Biep\" Durieux"
<mcvax!cs.vu.nl!biep@seismo.CSS.GOV>
Subject: Re: Garbage Collection Suppression
In article <8707202143.aa23792@Dewey.UDEL.EDU> Chester@UDEL.EDU writes:
>The direct way to avoid garbage collection in lisp is to define your own `cons'
>function that prefers to get cell pairs from an `available list' (...).
Also handy in many cases (small functions like append, alist-functions, subst)
is icons: (defun icons (a d cell)
(cond ((and (eq (car cell) a) (eq (cdr cell) d)) cell)
(t (cons a d))))
In this way whenever it turns out the new cells weren't really needed, the
old ones are used again (as in (append x nil)). Be aware, however, that your
copy-function may not work any more if it's defined as (subst nil nil x)!
--
Biep. (biep@cs.vu.nl via mcvax)
Never confound beauty with truth!
------------------------------
Date: Wed, 22 Jul 1987 10:43 EDT
From: MINSKY%OZ.AI.MIT.EDU@XX.LCS.MIT.EDU
Subject: Natural Kinds (Re: AIList Digest V5 #186)
About natural kinds. In "The Society of Mind", pp123-129, I propose a
way to deal with Wittgenstein's problem of defining terms like "game"-
or "chair". The basic idea was to probe further into what
Wittgenstein was trying to do when he talked about "family
resemblances" and tried to describe a game in terms of properties, the
way one might treat members of a human family: build, features, colour
of eyes, gait, temperament, etc.
In my view, Wittgenstein missed the point because he focussed on
"structure" only. What we have to do is also take into account the
"function", "goal", or "intended use" of the definition. My trick is
to catch the idea between two descriptions, structural and functional.
Consider a chair, for example.
STRUCTURE: A chair usually has a seat, back, and legs - but
any of them can be changed in so many ways that it is hard
to make a definition to catch them all.
FUNCTION: A chair is intended to be used to keep one's bottom
about 14 inches off the floor, to support one's back
comfortably, and to provide space to bend the knees.
If you understand BOTH of these, then you can make sense of that list
of structural features - seat, back, and legs - and engage your other
worldly knowledge to decide when a given object might serve well as a
chair. This also helps us understand how to deal with "toy chair" and
such matters. Is a toy chair a chair? The answer depends on what you
want to use it for. It is a chair, for example, for a suitable toy
person, or for reminding people of "real" chairs, or etc.
In other words, we should not worship Wittgenstein's final defeat, in
which he speaks about vague resemblances - and, in effect, gives up
hope of dealing with such subjects logically. I suspect he simply
wasn't ready to deal with intentions - because nothing comparable to
Newell and Simon's GPS theory of goals, or McCarthy's meta-predicate
(Want P) was yet available.
I would appreciate comments, because I think this may be an important
theory, and no one seems to have noticed it. I just noticed, myself,
that I didn't mention Wittgenstein himself (on page 130) when
discussiong the definition of "game". Apologies to his ghost.
------------------------------
Date: Wed, 22 Jul 87 12:40:58 EDT
From: mckee%corwin.ccs.northeastern.edu@RELAY.CS.NET
Subject: AI, science, and pseudo-science
In AIlist Digest v5 #171, July 6, 1987, Don Norman
<norman%ics@sdcsvax.ucsd.edu> wrote:
> [Here's why] many of us otherwise friendly folks in the sciences that
> neighbor AI [are] frustrated with AI's casual attitude toward theory:
> AI is not a science and its practitioners are woefuly untutored in
> scientific method."
[ 15 lines deleted ]
> AI worries a lot about methods and techniques, with many books and
> articles devoted to these issues. But by methods and techniques I
> mean such topics as the representation of knowledge, logic,
> programming, control structures, etc. None of this method includes
> anything about content. And there is the flaw: nobody in the field of
> Artificial Intelligence speaks of what it means to study intelligence,
> of what scientific methods are appropriate, what emprical methods are
> relevant, what theories mean, and how they are to be tested. All the
> other sciences worry a lot about these issues, about methodology,
> about the meaning of theory and what the appropriate data collection
> methods might be. AI is not a science in this sense of the word.
[ 22 more lines deleted ]
I think he's found an issue of critical importance here, so I'm going
to pull it out of context even further and repeat it again:
"nobody in the field of Artificial Intelligence speaks of what it means
to *study* intelligence" (my emphasis).
No wonder those of us outside the field have trouble figuring out
what AI is really about. My impression is that AI researchers try
to study intelligence by building artifacts that will make a convincing
show of intelligent behavior. This might be why books on AI methods are all
about sophisticated representations and fancy program structures -
they're techniques of building more complex (hopefully more intelligent)
programs. But this is nearsighted. Intelligence is the *difference*
between unintelligent and intelligent behavior. The study of intelligence
begins when the programming stops. And on what to do then, the AI textbooks
are silent.
Now I don't want to spend time talking about the consequences
of this failure, Don did that much better than I can. (However, I can't
resist throwing in my excuse: programming is fun; science is hard, often
boring, work. Science is far more rewarding, though.) What I'm going to
discuss in the rest of this note stems from his remark that AI workers
are "woefully untutored in scientific method". Assuming for the purposes
of discussion that we know enough about intelligence to make principled
distinctions between it and stupidity (counterintelligence?), what would
the scientific study of intelligence look like?
One way of answering this question is to look at some of the enterprises
that claim to be scientific, but aren't. The main distinction in the
list below is between those fields that are unarguably sciences, and those
that fail to be scientific in one way or another. True science, the authentic,
natural sciences, are ones like astronomy, geology, biology, physics, or
chemistry. False sciences are harder to characterize, but here goes:
Here's a list of examples of different claimants to the name "science";
mostly impostors, all of them can be called "quasi-sciences". By looking
at them, we can gain some sense of what qualities are necessary for
real sciences, since the quasi-sciences don't have them.
* Fraudulent sciences: Creation Science, Lysenkoism, Scientology
(the most generous thing I can say about these is that they
appear to proceed by trusting exceptional, one-of-a-kind
reports, and denying persistent, repeated, quantitative,
skeptical observations. In rhetoric this is called "appeal
to authority.")
* Trivial sciences: Clairol Science, barbeque science, accelerator science
(Clairol Science has discovered a new way to make your
hair silkier and more full-bodied. Barbeque science has
conclusively determined that mesquite smoke is superior to
hickory smoke. We need to build the superconducting supercollider
so America won't fall behind in accelerator science.)
* Semi-sciences: Theoretical Physics, Descriptive Linguistics
(complementary halves of their respective fields.)
* Interdisciplinary Sciences: Materials Science, Neuroscience
(characterized by their subject matter not yielding coherently
to any single experimental technique or theoretical paradigm.)
* Artifact Sciences: Economics, Political Science, Anthropology
(Herbert Simon's "sciences of the artificial" - these study artifacts
of human society - without civilization, they wouldn't exist.
However, civilization is big and complex enough that techniques
developed to deal with natural phenomena give useful insights.)
* Synthetic Sciences: Mathematics, Computer Science
(These study the consequences of small sets of fundamental concepts.
Mathematics under Russell&Whitehead and Bourbaki has been "nothing
but" an incredibly vast and elegant elaboration of set theory,
while [I claim with a certain trepidation] that the fundamental
basis of the scientific part of computer science lies in the
elaboration of the consequences of the notion of an algorithm.)
The authentic, natural sciences, on the other hand, are the body of analytic,
experimental studies of phenomena that go on whether or not the experimenter
is there to observe them, [philosophers can complain about "naive realism" --
I'll confess to the realism, but not not the naivete] and the results,
conclusions, and theoretical relations that tie the studies together.
The key concepts here are "experimental" and "objective". If a researcher
(or a team of them) isn't doing experiments on some external phenomenon,
then it ain't real science.
What do you get from real science? Reality. Not wishful thinking,
not hallucinations, not mythology, not common sense. (Strictly speaking,
what you get is the most compact model of reality consistent with the
most reliable, most detailed, widest ranging set of observations.)
Uncommon sense.
What you don't get is completeness, or even closure. First of all,
there's too much knowledge, as anyone with a Ph.D. in a natural science will
tell you. Second of all, the universe isn't closed under observation: there's
always more detail to examined, further frontiers to be explored, greater
complexities to be explained. And most exciting of all, there's the
possibility of revolution - that a new model will explain more data,
resolve old inconsistencies, or be statable more succinctly, hopefully
all at once.
The natural sciences generate an interconnected web of explanations
that should contain a place for AI, if AI is a science. It's in this
explanatory web that people claim to see the bugaboo of reductionism
(without which no discussion of scientific method would be complete).
Stripped of the argumentative mumbo-jumbo that keeps philosophers in business,
a reductionist would claim that a pile of parts on the floor is equivalent to
an assembled machine, while a holist would claim that the parts are irrelevant
to any description of the machine. Both views are incomplete, but there is
indeed an ordering by "is explained in terms of" that reductionists
have grabbed onto. Because it's only a partial ordering, I'd like to borrow
a term from evolutionary biology and suggest that scientific knowledge has
the same kind of familial, clade structure as do charts of the genetic
relations among organisms. Reading "<--" as "is used to explain", we have
One path through a Cladistic epistemology:
Particle Physics <--
Condensed-matter physics <--
Quantum Chemistry <--
Organic Chemistry <--
Molecular Biology/Genetics <--
Developmental Biology <--
Neuroscience <--
Ethology <--
Psychology <--
Cognitive Science <--
Mathematics
I would put intelligence in at the same level as mathematics. Congratulations!
Scientific AI would be among the most complex of sciences. However,
in reality the picture isn't this clean. Aside from those sciences that
aren't in a direct explanatory line to intelligence, there are shortcuts
among levels due to the logic of experimental science, that makes it possible
to do things like manipulate genetic structure and get a behavioral result.
But this note is already too long to go into this further, and I've barely
alluded to the formal role of the hypothesis.
Hope this helps,
- George McKee
College of Computer Science
Northeastern University, Boston 02115
CSnet: mckee@Corwin.CCS.Northeastern.EDU
Phone: (617) 437-5204
Usenet: in New England, it's not unusual to have to say
"can't get there from here."
------------------------------
End of AIList Digest
********************
