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From: tgd@ORSTCS.CS.ORST.EDU.UUCP
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Subject: Re: Lenat's AM program
Message-ID: <774@orstcs.CS.ORST.EDU>
Date: Wed, 28-Oct-87 13:04:45 EST
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Posted: Wed Oct 28 13:04:45 1987
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David West (dwt@zippy.eecs.umich.edu) writes:

    Some possible contributing reasons for this sort of difficulty in AI:

    1) The practitioners of AI routinely lack access at the nuts-and-bolts level 
    to the products of others' work. (At a talk he gave here three years ago,
    Lenat said that he was preparing a distribution version of AM.  Has
    anyone heard whether it is available? I haven't.)   Perhaps widespread
    availability and use of Common Lisp will change this.  Perhaps not.

In the biological sciences, publication of an article reporting a new
clone obligates the author to provide that clone to other researchers
for non-commercial purposes.  I think we need a similar policy in
computer science.  Publication of a description of a system should
obligate the author to provide listings of the system (a running
system is probably too much to ask for) to other researchers on a
non-disclosure basis. 

    2) The supporting institutions (and most practitioners) have little 
    patience for anything as unexciting and 'unproductive' as slow, 
    painstaking post-mortems.
    3) We still have no fruitful paradigm for intelligence and discovery.
    4) We are still, for the most part, too insecure to discuss difficulties
    and failures in ways that enable others as well as ourselves to learn
    from them.  (See an article on the front page of the NYTimes book review
    two or three weeks ago for a review of a book claiming that twentieth-
    century science writing in general is fundamentally misleading in this 
    respect.)

I disagree with these other points.  I think the cause of the problem
is lack of methodological training for AI and CS researchers.  Anyone
could have reimplemented an approximation of AM based on the published
papers anytime in the past decade.  I think the fact that people are
now beginning to do this is a sign that AI is becoming
methodologically healthier.  A good example is the paper Planning for
Conjunctive Goals by D. Chapman in Artificial Intelligence, Vol 32,
No. 3, which provides a critical review and rational reconstruction of
the NOAH planning system.  I encourage all students who are looking
for dissertation projects to consider doing work of this kind.

--Tom