Subject: From: AIList Moderator Kenneth Laws <AIList-REQUEST%sri-ai.arpa@CSNET-RELAY>

From: [email-masked]
Date: Sat, 8 Feb 86 01:35:54 est

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Subject: AIList Digest   V4 #21
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AIList Digest             Friday, 7 Feb 1986       Volume 4 : Issue 21

Today's Topics:
  Queries - ISIS & BIB-Format AI References,
  Logic Programming - Prolog for Compiler Writing,
  Expert Systems & Reports - MRS,
  Theory - Dreyfus Article in Technology Review

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Date: Tue, 04 Feb 86 16:19:44 cet
From: [email-masked]
Subject: Query -- ISIS

From: Uffe K. Mortensen ESA ( The European Space Agency )

Does anybody here know what 'ISIS' is ? I have been told it is a commercial
package for planning/scheduling problems, but I would like to have more
detailed information ( vendor, etc ).

-- Uffe.

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Date: 3 Feb 86 18:59:00 GMT
From: [email-masked]
Subject: Bib Format AI References Request

I am interested in getting references in bib format for the following
topics, ordered with the stuff most important to me now on top:
        * distributed problem solving
        * machine learning
        * planning
        * vision
If you have any references available, please send them to:
        mklein@uiucdcsb
                        Thanks!
                                Mark Klein

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Date: 06 Feb 86 09:37:54 +1100 (Thu)
From: Isaac Balbin <[email-masked]>
Subject: Re: Prolog for Compiler Writing

I have not added compilers for prolog written in prolog, nor stuff on
compiling techniques for prolog.

%A H. Derby
%T Using Logic Programming for Compiling APL
%R Technical Report 84-5134
%I Department of Computer Science
%I California Institute of Technology
%C Los Angeles, California
%D 1984

%A G.A. Edgar
%T A Compiler Written in Prolog
%J Dr. Dobbs Journal
%D May, 1985

%A Harald Ganzinger
%A Michael Hanus
%T Modular Logic Programming of Compilers
%J Proceedings of the 2nd IEEE International Symposium on Logic Programming
%C Boston, USA
%D July, 1985

%A D.H.D. Warren
%T Logic for Compiler Writing
%J Software Practice and Experience
%V 10
%N 1
%P 97-125
%D 1980
%O Also available as DAI Research Paper 44
from Department of Artificial Intelligence, University of Edinburgh

                Isaac Balbin
===========================
UUCP:   {seismo,mcvax,ukc,ubc-vision}!munnari!isaac
ARPA:   [email-masked]
CSNET:  isaac%munnari.oz@australia

------------------------------

Date: Tue, 4 Feb 86 15:46:28 EST
From: [email-masked] (Warwick Bolam)
Subject: Correction to correction to name of MRS

  >From: [email-masked]
  >
  >In a recent issue the full name MRS was incorrectly reported.
  >
  >        MRS = "Modifiable Representation System"
  >
  >        (source - "MRS Manual", Michael R. Genesereth, et. al.
  >                1980, Stanford Heuristic Programming Project)

In the bibliography of the paper "Partial Programs", Michael R Genesereth,
1984, Stanford HPP:

        M. R. Genesereth, R. Greiner, D. E. Smith: "MRS - A Meta-Level
        Representation System", HPP-83-27, Stanford University HPP, 1983.

Is there anyone who REALLY knows what MRS stands for?  I have a number of
MRS documents and NONE of them says "MRS stand for ..."

Warwick Bolam,
Computing Dept, Royal Melbourne Institute of Technology,
Melbourne, Victoria, Australia.

------------------------------

Date: Mon 3 Feb 86 17:15:26-PST
From: Stuart Russell <[email-masked]>
Subject: MRS manual

I have been asked to point out to those who have requested copies of
the MRS manual, or who intend to do so, that a nominal fee of $6.00
(plus tax if in CA) is suggested. At .004 cents per exqusitely chosen word,
it's a bargain.
Stuart Russell (RUSSELL@SUMEX)

------------------------------

Date: 4 Feb 86 13:30:31 GMT
From: Bob Stine <[email-masked]>
Subject: re: Dreyfus article

"Why Computers May Never Think Like People," a recent
diatribe by the brothers Dreyfus, has several problems.
First and foremost is that AI research is implicitly
identified as the development of rule-based systems.
All of the well known limitations of rule-based systems
are inappropriately attributed to AI research as a whole.

There is a deeper problem with the article, that perhaps
springs from a misguided humanism.  The article claims
that machines will never duplicate human performance in
cognitive tasks, because humans have "intuition."  These
passages would read very much the same if 'magic' were
substituted for 'intuition' - "Human begins have a
magic intelligence that reasoning machines simply cannot
match."  "... a boxer seems to recognize the moment to
begin an attack... ... the boxer is using his magic".

The Dreyfus brothers claim that they are not "Luddites,"
that they are not opposed to technology per se, but just
to wasting time and money on AI research.  The basis of
their position is that some aspect of human intelligence
is inherently beyond human comprehension.

There certainly are things that humans will never know.
But no one thing is inherently unknowable.

------------------------------

Date: Tue, 4 Feb 86 09:58:53 CST
From: [email-masked] (Pete Sandon)
Subject: Knowledge Aquisition -vs- Skill Refinement

   This is not to defend the Dreyfus brothers, since I have yet to read
their books. On the other hand, I think they make a good point, though
with a bad example, in emphasizing learning as a process of refinement.
The example related in Miles Murdocca's submission is that of learning
to ride a bike through trial and error. The reason the example is a bad
one, is that it fits into the category of skill refinement as AI
researchers would use the term. This leads to the argument that Dreyfus
and Dreyfus are missing the critical distinction between knowledge
acquisition and skill refinement.

   My feeling is that too much is made of this distinction. Had the
example been one of learning to distinguish fruits from vegetables,
or one of learning the symptoms of a class of diseases well enough
to diagnose them, this argument would not have arisen. Clearly these
involve knowledge acquisition rather than skill refinement. And yet, it
could be argued, and perhaps is argued by the Dreyfus's, that what
the AI researchers consider to be knowledge acquisition should be
just as much a refinement process guided by trial and error as learning
to ride a bike. Whereas AI considers concept formation to occur as the
acquisition of discrete chunks of knowledge, an alternative is to use
the gradual acquisition of evidence to support one concept definition
over another, in a manner similar to skill refinement.

   Of course, if this criticism of AI is correct, AI has already
answered it. The use of connectionist models, and the corresponding
learning mechanisms currently being studied, provide just the sort
of cognitive models that support this refinement type of learning
through trial and error.

--Pete Sandon

------------------------------

Date: 3 Feb 86 17:24:42 GMT
From: [email-masked]
Subject: Re: Technology Review article

In article <[email-masked]>, [email-masked] writes:
>
> Has anyone read the article about AI in the February issue of
> "Technology Review"?  You can't miss it -- the cover says something
> like: "In 25 years, AI has still not lived up to its promises and
> there's no reason to think it ever will" (not a direct quote; I don't
> have the copy with me).  General comments?
>
This article is a plug for a book and  use of a current topic to get back at
the AI community for an imagined snub.  Hubert Dreyfus was stood up by
John McCarthy of Stanford at a debate on a third echelon public tv
station in the bay area, and is still mad.
First, the premise:  AI, expert systems, and knowledge-rule based systems
have been overly optimistic in their promises and stand short of delivered
results.  Probably true, but many of the systems, once implemented, lose
their mystical qualities, and look a lot like other computer applications.
It's the ones that are in the buliding process which seem to present
extravagant claims.
As presented, however, the article is a shrill cry rather than a reasoned
response.  It leans heavily on proof by intense assertion.  As a pilot
I find examples which range from dubious to incorrect.  As a scientist I
object to the gee whiz Reader's Digest tone.  As a retired Air Force Officer
I object to the position that the commander's common sense is the ideal form
of combat decision making.  And as a philosopher (albeit not expert) I object
to the muddy intellectual approach, rife with questionable presuppositions,
faulty dilemmas, and illogical conclusions.
I agree that the topic is worthy of discussion-  our work to realize the
potential of computers must not degenerate into a fad which will fade
from the scene.  But I object to a diatribe where advances in the field
are dismissed as trivial because current systems do not equal human
performance.
--
       ...Pete                  Peter G. Cook                      Lt. Colonel
[email-masked]                  Liaison, Motorola, Inc.            USAFR(Ret)
ut-sally!im4u!milano!pcook      MCC-Software Technology Program
512-834-3348                    9430 Research Blvd. Suite 200
                                Austin, Texas 78759

  [There are, of course, two sides to the McCarthy incident.  As I recall
  from an old SU-BBoard message, McCarthy had agreed to an interview under
  the impression that he would be on the program alone.  At the last moment
  it was mentioned that Dreyfus had also been invited.  Viewing this as "ambush
  journalism" -- my words -- McCarthy declined to participate in the impromptu
  debate.  No doubt the station was just trying to schedule a lively evening,
  but they should have checked with McCarthy and given him time to prepare.
  He and Dreyfus have sufficient visibility that a poorly stated remark, on
  >>any<< radio station, could affect the future of AI funding.  -- KIL]

------------------------------

Date: Thu, 6 Feb 86 08:46 EST
From: Ken Haase <[email-masked]>
Subject: Re: Technology Review article

    Date: 3 Feb 86 14:25:24 GMT
    From: [email-masked]  (Miles Murdocca)
    Subject: Re: Technology Review article
    To: AIList@SRI-AI

    The [Technology Review] article was written by the Dreyfuss brothers,
    who are famous for making bold statements that AI will never meet the
    expectations of the people who fund AI research.  They make the claim
    that people do not learn to ride a bike by being told how to do it,
    but by a trial and error method that isn't represented symbolically.
    They use this argument and a few others such as the lack of a
    representation for emotions to support their view that AI researchers
    are wasting their sponsors' money by knowingly heading down dead-ends.

I don't think the Dreyfus brothers accuse AI researches of knowingly
heading down dead-ends.  They just claim that most of ``what people do''
cannot be captured by the ``abstracted representations'' of nearly all
current AI research.  I don't agree with this claim, but can't deny that
we (in AI) may be all wrong about our central hypothesis.  We just have
to make our hypothesis clear and explicit.  I think that most high level
intellectual processes have effective symbolic representations (and I'm
working to find out what such representations might be).  That is an
explicit hypothesis of my research.  On the other hand, I do not think
that there is anything like a symbolic representation of ``how to ride a
bike''.  What happens in such cases is that our intellect ``trains'' the
animal that is the rest of us to ride the bicycle.

    As I recall ["Machine Learning", Michalski et al, Ch 1], there are two
    basic forms of learning: 'knowledge acquisition' and 'skill refinement'.
    The Dreyfuss duo seems to be using a skill refinement problem to refute
    the work going on in knowledge acquisition.  The distinction between the
    two types of learning was recognized by AI researchers years ago, and I
    feel that the Dreyfuss two lack credibility since they fail to align their
    arguments with the taxonomy of the field.

The alchemists could have made the same argument against arguments for
the periodic table; what the Dreyfus brothers are arguing for is the
need for just such a ``paradigm shift'' in cognitive science.  The fact
that this shift will disrupt the foundations of most current AI
technology (most of which is not well proven anyway) should not effect
scientific judgements at all (though, pessimistically, it certainly
will).

In any case, the dichotomy between skill refinement and knowledge
acquisition is even suspect; outside of rote learning of facts, most
gained knowledge is gained by appropriating the knowledge as skills (in
a broad sense of skills, which includes responses, perceptual skills,
etc).

Ken

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End of AIList Digest
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