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AIList Digest Volume 5 Issue 256
AIList Digest Tuesday, 3 Nov 1987 Volume 5 : Issue 256
Today's Topics:
Reference - Chaos Theory,
Bindings - Langendoen and Postal & Netmail to UK,
Analogy - Knowledge Soup & Robert Frost,
Inference - Prediction-Producing Algorithms
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Date: Mon, 2 Nov 87 16:04 N
From: MFMISTAL%HMARL5.BITNET@wiscvm.wisc.edu
Subject: re: The success of AI (misunderstandings) - CHAOS theory
The august 1987 issue of the proceedings of the IEEE contains 9 papers
on chaotic systems It has a tutorial for engineers, 3 papers with
examples in electronic circuits, 2 papers on analytical tools and
3 papers on software and hardware tools.
Jan L. Talmon
University of Limburg, Dept. of Medical Informatics and Statistics.
Maastricht, the Netherlands
MFMISTAL@HMARL5.bitnet
------------------------------
Date: 2 Nov 87 17:00:55 GMT
From: sunybcs!rapaport@ames.arpa (William J. Rapaport)
Subject: Re: Langendoen and Postal (posted by: Berke)
In article <8941@shemp.UCLA.EDU> berke@CS.UCLA.EDU (Peter Berke) writes:
>I just read this fabulous book over the weekend, called "The Vastness
>of Natural Languages," by D. Terence Langendoen and Paul M. Postal.
>
>Are Langendoen or Postal on the net somewhere?
Langendoen used to be on the net as: tergc%cunyvm@wiscvm.wisc.edu
but he's moved to, I think, U of Arizona. Postal, I think, used to be
at IBM Watson.
------------------------------
Date: Thu, 29 Oct 87 16:13:01 GMT
From: "G. Joly" (Birkbeck) <gjoly@NSS.Cs.Ucl.AC.UK>
Subject: Re: transatlantic netmail mail to UK.
Pat Hayes has given us some propaganda. Yorick Wilkes informed us that
he cannot send mail, although he used to be able to do so.
If I can add may 1.34564 cents worth, the real issue is that the
ARPA tables (from SRI-NIC) do not allow a path to UCL-CS.ARPA and
beyond. This gateway is now known as nss.cs.ucl.ac.uk and nothing
else will work.
I am not a network person at UCL; they inform me that an official
response will be prepared (I am fairly sure that the unsigned note
to Pat was not it). The change away from UCL-CS.ARPA was advertised
at least two years ago.
"The plans have been on view at the planning office on ... "
after Douglas Adams.
Gordon Joly,
Computer Science,
Birkbeck College,
Malet Street,
LONDON WC1E 7HX.
+44 1 631 6468
ARPA: gjoly@nss.cs.ucl.ac.uk
BITNET: UBACW59%uk.ac.bbk.cu@AC.UK
UUCP: ...!seismo!mvcax!ukc!bbk-cs!gordon
------------------------------
Date: 28 October 1987, 20:02:20 EST
From: john Sowa <SOWA@ibm.com>
Subject: Knowledge Soup
Since my abstract on "Crystallizing Theories out of Knowledge Soup"
appeared in AIList V5 #241 and my clarification appeared in V5 #247,
I have received a number of requests for the corresponding paper.
I regret to say that the paper is still in the process of getting
itself crystallized. That talk was mostly a survey of current
approaches to the soup together with some suggestions about techniques
that I considered promising. Following is what I discussed:
1. The limits of conceptualization and the use of conceptual analysis
as a nonautomated way of extracting knowledge from the soup. This
material is discussed in my book, Conceptual Structures. See
Section 6.3 for conceptual analysis, and Chapter 7 for a discussion
of the limitations.
2. Dynamic belief revision, developed by Norman Foo and Anand Rao
from Sydney University, currently visiting IBM. This is a kind of
truth maintenance system based on the axioms for belief revision
by the Swedish logician Gardenfors. They have been adding some
interesting features, including levels of epistemic importance
(laws, facts, and defaults) where the revision process tries to
retain the more important propositions at the expense of losing
some of the less important. Their current system uses Prolog
style rules and facts, but they are adapting it to conceptual
graphs as part of CONGRES (their conceptual graph reasoning system).
3. Dynamic type hierarchies, an idea developed by Eileen Way in
her dissertation on metaphor. As in most treatments of metaphor,
Eileen compares matching relationships in the tenor and vehicle
domains. Her innovation is the recognition that the essential
meaning of a metaphor is the introduction of a new node in the
type hierarchy.
Example: "My car is thirsty." The canonical graph for THIRSTY
shows that it must be an attribute of something of type ANIMAL.
Since CAR is not a subtype of ANIMAL, the system finds a minimal
common supertype of CAR and ANIMAL, in this case MOBILE-ENTITY.
It then creates a new node in the type hierarchy above both
CAR and ANIMAL, but below MOBILE-ENTITY. To create a definition
for that type, it checks the properties of ANIMAL with respect to
THIRSTY, and finds a graph saying that THIRSTY is an attribute of
an ANIMAL that is in the sate of needing liquid:
[THIRSTY]<-(ATTR)<-[ANIMAL]->(STAT)->[NEED]->(PTNT)->[LIQUID]
It then generalizes ANIMAL to MOBILE-ENTITY and uses the resulting
graph to define a new type for mobile entities that need liquid.
The system can generalize schemata involving animals and liquid
to the new node, from which they can be inherited by CAR or any
similar subtype. The new node thereby allows schemata for DRINK
or GUZZLE to be inherited as well as schemata for THIRSTY.
4. Theory refinement. This is an approach that I have been discussing
with Foo and Rao as an extension to their belief revision system.
Instead of making revisions by adding and deleting propositions,
as they currently do, the use of conceptual graphs allows individual
propositions or even parts of propositions to be generalized or
specialized by adding and deleting parts or by moving up and down
the type hierarchy. This extension can still be done within the
framework of the Gardenfors axioms. As the topic changes, the
salience of different concepts and patterns of concepts in the
knowledge soup changes. The most salient ones become candidates
for crystallization out of the soup into the formalized theory.
The knowledge soup thus serves as a resource that the belief
revision process draws upon in constructing the crystallized
theories. Depending on the salience, different theories can be
crystallized from the same soup, each representing a different
point of view. Even though the soup may be inconsistent, each
theory crystallized from it is consistent, but specialized for
a limited domain.
People are capable of precise reasoning, but usually with short chains
of inference. They are also capable of dealing with enormous, but
loosely organized collections of knowledge. Instead of viewing formal
theories and informal associative techniques as competing or conflicting
approaches, I view them as complementary mechanisms that should be made
to cooperate. This talk discussed possible ways of doing that. Although
there is an enormous amount of work that remains to be done, there are
also some promising directions for future research.
References:
Foo, Norman Y., & Anand S. Rao (1987) "Open world and closed world
negations," Report RC 13122, IBM T. J. Watson Research Center.
Foo, Norman Y., & Anand S. Rao (in preparation) "Semantics of
dynamic belief systems."
Foo, Norman Y., & Anand S. Rao (in preparation) "Belief and ontology
revision in a microworld.
Rao, Anand S., & Norman Y. Foo (1987) "Evolving knowledge and logical
omniscience," Report RC 13155, IBM T. J. Watson Research Center.
Rao, Anand S., & Norman Y. Foo (1987) "Evolving knowledge and
autoepistemic reasoning," Report RC 13155, IBM T. J. Watson Research
Center.
Rao, Anand S., & Norman Y. Foo (1986) "Modal horn graph resolution,"
Proceedings of the First Australian AI Congress, Melbourne.
Rao, Anand S., & Norman Y. Foo (1986) "DYNABELS -- A dynamic belief
revision system," Report 301, Basser Dept. of Computer Science,
University of Sydney.
Sowa, John F. (1984) Conceptual Structures: Information Processing in
Mind and Machine, Addison-Wesley, Reading, MA.
Way, Eileen C. (1987) Dynamic Type Hierarchies: An Approach to
Knowledge Representation through Metaphor, PhD dissertation,
Systems Science Dept., SUNY at Binghamton.
For copies of the IBM reports, write to Distribution Services 73-F11;
IBM T. J. Watson Research Center; P.O. Box 218; Yorktown Heights,
NY 10598.
For the report from Sydney, write to Basser Dept. of Computer Science;
University of Sydney; Sydney, NSW 2006; Australia.
For the dissertation by Eileen Way, write to her at the Department
of Philosophy; State University of New York; Binghamton, NY 13901.
------------------------------
Date: 30 Oct 87 11:11:24 EST (Fri)
From: sas@bfly-vax.bbn.com
Subject: Robert Frost
I am forwarding this without permission from the 23 October 1987 issue
of Science:
Robert Frost on Thinking
Readers intrigured by "Causality, structure, and common sense" by M.
Mitchell Waldrop (Research News, 11 Sept., p1297) may be interested in
knowing that the role of analogy in reasoning has been discussed
eloquently by poet Robert Frost in an essay called "Education by
poetry". The following excerpts are among his most relevant comments:
"I have wanted in late years to go further and further in making
metaphor the whole of thinking. I find some one now and then to agree
with me that all thinking, except mathematical thinking, is
metaphorical, or all thinking except scientific thinking. The
mathematical might be difficult for me to bring in, but the scientific
is easy enough...."
"What I am pointing out is that unless you are at home in the
metaphor, unless you have had your proper poetical education in the
metaphor, you are not safe anywhere. Because you are not at ease with
figurative values: you don't know the metaphor in its strength and its
weakness. You don't known how far you may expect to ride it and when
it may break down with you. You are not safe in sciencel; you are not
safe in history...."
"... All metaphor breaks down somewhere. That is the beauty of it.
It is touch and go with the metaphor, and until you have lived with it
long enough you don't know when it is going. You don't know how much
you can get out of it and when it will cease to yield. It is a very
living thing. It is as life itself...."
"We still ask boys in college to think, as in the nineties, but we
seldom tell them what thinking means; we seldom tell them it is just
putting this and that together; it saying one thing in terms of
another. To tell them is to set their feet on the first rung of a
ladder the top of which sticks through the sky."
Perhaps researchers in artificial intelligence who are teaching
computers to reason by analogy should include in their curriculum a
course in poetry. If so, I suggest they start with Frost. His poems
have become an improtant feature of my own ecology courses because
they contain much insight into cause and effect in nature, rather than
mere appearance.
Dan M. Johnson
Dept of Biological Sciences
East Tennessee State University
Johnson City, TN 37614
------------------------------
Date: 30 Oct 87 0950 PST
From: John McCarthy <JMC@SAIL.Stanford.EDU>
Subject: Prediction-producing Algorithms
Eliot Handleman's request for information on prediction has
inspired me to inflict the following considerations on the community.
Roofs and Boxes
Many people have proposed sequence extrapolation as a prototype AI
problem. The idea is that a person's life is a sequence of sensory
stimuli, and that science consists of inventing ways of predicting the
future of this sequence. To this end many sequence extrapolating programs
have been written starting with those that predict sequences of integers
by taking differences and determining the co-efficients of a polynomial.
It has always seemed to me that starting this way distorts the
heuristic character of both common sense and science. Both of them think
about permanent aspects of the world and use the sequence of sense data
only to design and confirm hypotheses about these permanent aspects. The
following sequence problem seems to me to typify the break between
hypotheses about the world and sequence extrapolation.
The ball bouncing in the rectilinear world - roofs and boxes
Suppose there is a rectangular two dimensional room. In this room
are a number of objects having the form of rectangles. A ball moves in
the room with constant velocity but bounces with angle of incidence equal
to angle of reflection whenever it hits a wall or an object. The observer
cannot see the objects or the walls. All he sees is the x-co-ordinate of
the ball at integer times but only when the ball is visible from the front
of the room. This provides him with a sequence of numbers which he can
try to extrapolate. Until the ball bounces off something or goes under
something, linear extrapolation works.
Suppose first that the observer knows that he is dealing with this
kind of ball-in-room problem and only doesn't know the locations of the
objects and the walls. After he has observed the situation for a while he
will have partial information about the objects and their locations. For
example, he may note that he has never been in a certain part of the room
so there may be unknown objects there. Also he may have three sides of a
certain rectangle but may not know the fourth side, because he has never
bounced of that side yet. He may extrapolate that he won't have the
opportunity of bouncing off that side for a long time.
Alternatively we may suppose that the observer doesn't
initially know about balls bouncing off rectangles but only knows
the sequence and must infer this using a general sequence extrapolation
mechanism. Our view is that this observer, whether human or machine,
can make progress only by guessing the underlying model. At first
he may imagine a one dimensional bouncing model, but this will be
refuted the first time the ball doesn't bounce at an x-co-ordinate
where it has previously bounced. Indeed he has to keep open
the possibility that the room is really 3 or more dimensional or that
more general objects than rectangles exist.
We can elaborate the problem by supposing that when the ball
bounces off the front wall, the experimenter can put a paddle at an angle
and determine the angly of bounce so as to cause the ball to enter regions
where more information is wanted.
Assuming the rectangles having edges parallel to the axes makes
the problem easier in an obvious sense but more difficult in the sense
that there is less interaction between the observable x-co-ordinate and
the unobservable y-co-ordinate.
It would be interesting to determine the condition on the x-path
that distinguishes 2-dimensional from 3-dimensional worlds, if there is
one. Unless we assume that the room has some limited size, there need be
no distinction. Thus we must make the never-fully-verified assumption
that some of the repetititions in sequences of bounces are because the
ball hit the front or back wall and bounced again off the same surfaces
rather than similar surfaces further back.
A tougher problem arises when the observer doesn't get the
sequence of x-coordinates but only 1 or 0 according to whether the
ball is visible or invisible.
I am skeptical that an AI program fundamentally based on the idea
of sequence extrapolation is the right idea. Donald Michie suggested
that the "domain experts" for this kind of problem of inferring a
mechanism that produces a sequence are cryptanalysts.
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End of AIList Digest
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