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AIList Digest Volume 1 Issue 117
AIList Digest Friday, 30 Dec 1983 Volume 1 : Issue 117
Today's Topics:
Reply - Fiber Optic Camera,
Looping Problem - Loop Detection and Classical Psychology,
Logic Programming - Horn Clauses, Disjunction, and Negation,
Alert - Expert Systems & Molecular Design,
AI Funding - New Generation Discussion,
Science - Definition
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Date: 23 Dec 1983 11:59-EST
From: David.Anderson@CMU-CS-G.ARPA
Subject: fiber optic camera?
The University of Pittsburgh Observatory is experimenting with just
such an imaging system in one of their major projects, trying to
(indirectly) observe planetary systems around nearby stars. They claim
that the fiber optics provide so much more resolution than the
photography they used before that they may well succeed. Another major
advantage to them is that they have been able to automate the search;
no more days spent staring at photographs.
--david
------------------------------
Date: Fri 23 Dec 83 12:01:07-EST
From: Michael Rubin <RUBIN@COLUMBIA-20.ARPA>
Subject: Loop detection and classical psychology
I wonder if we've been incorrectly thinking of the brain's loop detection
mechanism as a sort of monitor process sitting above a train of thought,
and deciding when the latter is stuck in a loop and how to get out of it.
This approach leads to the problem of who monitors the monitor, ad
infinitum. Perhaps the brain detects loops in *hardware*, by classical
habituation. If each neuron is responsible for one production (more or
less), then a neuron involved in a loop will receive the same inputs so
often that it will get tired of seeing those inputs and fire less
frequently (return a lower certainty value), breaking the loop. The
detection of higher level loops such as "Why am I trying to get this PhD?"
implies that there is a hierarchy of little production systems (or
whatever), one for each chunk of knowledge. [Next question - how are
chunks formed? Maybe there's a low-level explanation for that too, having
to do with classical conditioning....]
BTW, I thought of this when I read some word or other so often that it
started looking funny; that phenomenon has gotta be a misfeature of loop
detection. Some neuron in the dictionary decides it's been seeing that damn
word too often, so it makes its usual definition less certain; the parse
routine that called it gets an uncertain definition back and calls for
help.
--Mike Rubin <Rubin@Columbia-20>
------------------------------
Date: 27 Dec 1983 16:30:08-PST
From: marcel.uiuc@Rand-Relay
Subject: Re: a trivial reasoning problem?
This is an elaboration of why a problem I submitted to the AIList seems
to be unsolvable using regular Horn clause logic, as in Prolog. First I'll
present the problem (of my own devising), then my comments, for your critique.
Suppose you are shown two lamps, 'a' and 'b', and you
are told that, at any time,
1. at least one of 'a' or 'b' is on.
2. whenever 'a' is on, 'b' is off.
3. each lamp is either on or off.
WITHOUT using an exhaustive generate-and-test strategy,
enumerate the possible on-off configurations of the two
lamps.
If it were not for the exclusion of dumb-search-and-filter solutions, this
problem would be trivial. The exclusion has left me baffled, even though
the problem seems so logical. Check me on my thinking about why it's so
difficult.
1. The first constraint (one or both lamps on) is not regular Horn clause
logic. I would like to be able to state (as a fact) that
on(a) OR on(b)
but since regular Horn clauses are restricted to at most one positive
literal I have to recode this. I cannot assert two independent facts
'on(a)', 'on(b)' since this suggests that 'a' and 'b' are always both
on. I can however express it in regular Horn clause form:
not on(b) IMPLIES on(a)
not on(a) IMPLIES on(b)
As it happens, both of these are logically equivalent to the original
disjunction. So let's write them as Prolog:
on(a) :- not on(b).
on(b) :- not on(a).
First, this is not what the disjunction meant. These rules say that 'a'
is provably on only when 'b' is not provably on, and vice versa, when in
fact 'a' could be on no matter what 'b' is.
Second, a question ?- on(X). will result in an endless loop.
Third, 'a' is not known to be on except when 'b' is not known to be on
(which is not the same as when 'b' is known to be off). This sounds as
if the closed-world assumption might let us get away with not being able
to prove anything (if we can't prove something we can always assume its
negation). Not so. We do not know ANYTHING about whether 'a' or 'b' are
on OR off; we only know about constraints RELATING their states. Hence
we cannot even describe their possible states, since that would require
filling in (by speculative hypothesis) the states of the lamps.
What is wanted is a non-regular Horn clause, but some of the nice
properties of Logic Programming (eg completeness and consistency under the
closed-world assumption, alias a reasonable negation operator) do not apply
to non-regular Horn clauses.
2. The second constraint (whenever 'a' is on, 'b' is off) shares some of the
above problems, and a new one. We want to say
on(a) IMPLIES not on(b), or not on(b) :- on(a).
but this is not possible in Prolog; we have to say it in what I feel to
be a rather contrived manner, namely
on(b) :- on(a), !, fail.
Unfortunately this makes no sense at all to a theoretician. It is trying
to introduce negative information, but under the closed-world assumption,
saying that something is NOT true is just the same as not saying it at all,
so the clause is meaningless.
Alternative: define a new predicate off(X) which is complementary to on(X).
That is the conceptualization suggested by the third problem constraint.
3. off(X) :- not on(X).
on(X) :- not off(X).
This idea has all the problems of the first constraint, including the
creation of another endless loop.
It seems this problem is beyond the capabilities of present-day logic
programming. Please let me know if you can find a solution, or if you think
my analysis of the difficulties is inaccurate.
Marcel Schoppers
U of Illinois at Urbana-Champaign
{pur-ee|ihnp4}!uiucdcs!marcel
------------------------------
Date: Mon 26 Dec 83 22:15:06-PST
From: Ken Laws <Laws@SRI-AI.ARPA>
Subject: High Technology Articles
The January issue of High Technology has a fairly good introduction
to expert systems for commercial applications. As usual for this
magazine, there are corporate names and addresses and product
prices. The article mentions that there are probably fewer than
200 "knowledge engineers" in the country, most at universities
and think tanks; an AI postdoc willing to go into industry, but
with no industry experience, can command $70K.
The business outlook section is not the usual advice column
for investors, just a list of some well-known AI companies. The
article is also unusual in that it bases a few example of knowledge
representation and inference on the fragment BIRD IS-A MAMMAL.
Another interesting article is "Designing Molecules by Computer".
Several approaches are given, but one seems particularly pertinent
to the recent AIList discussion of military AI funding. Du Pont
researchers are studying how a drug homes in on its receptor site.
They use an Army program that generates line-of-sight maps for
TV-controlled antitank missiles to "fly" a drug in and observe how its
ability to track its receptor site on the enzyme surface is influenced
by a variety of force fields and solvent interactions. A different
simulation with a similar purpose uses robotic software for assembling
irregular components to "pick up" the drug and "insert" it in the
enzyme.
-- Ken Laws
------------------------------
Date: 23 December 1983 21:41 est
From: Dehn at MIT-MULTICS (Joseph W. Dehn III)
Subject: "comparable" quotes
Person at University of Tokyo, editor of a scientific/engineering
journal, says computers will be used to solve human problems.
Person at DARPA says computers will be used to make better weapons
("ways of killing people").
Therefore, Japanese are humane, Americans are warmongers.
Huh?
What is somebody at DARPA supposed to say is the purpose of his R&D
program? As part of the Defense Department, that agency's goal SHOULD
be to improve the defense of the United States. If they are doing
something else, they are wasting the taxpayer's money. There are
undoubtedly other considerations involved in DARPA's activities,
bureaucratic, economic, scientific, etc., but, nobody should be
astonished when an official statement of purpose states the official
purpose!
Assuming the nation should be defended, and assuming that advanced
computing can contribute to defense, it makes sense for the national
government to take an interest in advanced computing for defense. Thus,
the question should not be, "why do Americans build computers to kill
people", but rather why don't they, like the Japanese, ALSO, and
independent of defense considerations (which are, as has been pointed
out, different in Japan), build computers " to produce profitable
industrial products"?
Of course, before we try to solve this puzzle, we should first decide
that there is something to be solved. Is somebody suggesting that
because there are no government or quasi-government statements of
purpose, that Americans are not working on producing advanced and
profitable computer products? What ARE all those non-ARPA people doing
out there in netland, anyway? Where are IBM's profits coming from?
How can we meaningfully compare the "effort" being put into computer
research in Japan and the U.S.? Money? People? How about results?
Which country has produced more working AI systems (you pick the
definition of "working" and "AI")?
-jwd3
------------------------------
Date: 29 Dec 1983 09:11:34-PST
From: Mike Brzustowicz <mab@aids-unix>
Subject: Japan again.
Just one more note. Not only do we supply Japan's defense, but by treaty
they cannot supply their own (except for a very small national guard-type
force).
------------------------------
Date: 21 Dec 83 19:49:32-PST (Wed)
From: harpo!eagle!mhuxl!ulysses!princeton!eosp1!robison @ Ucb-Vax
Subject: Re: Information sciences vs. physical sc - (nf)
Article-I.D.: eosp1.466
I disagree - astronomy IS an experimental science. Even before the
age of space rockets, some celebrated astronomical experiments have
been performed. In astronomy, as in all sciences, one observes,
makes hypotheses, and then tries to verify the hypotheses by
observation. In chemistry and physics, a lot of attention is paid
to setting up an experiment, as well as observing the experiment;
in astronomy (geology as well!), experiments consist mostly
of observation, since there is hardly anything that people are capable
of setting up. Here are some pertinent examples:
(1) An experiment to test a theory about the composition of the sun has
been going on for several years. It consists of an attempt to trap
neutrinos from the sun in a pool of chlorine underground. The amount
of neutrinos detected has been about 1/4 of what was predicted, leading
to new suggestions about both the composition of the sun,
and (in particle physics) the physical properties of neutrinos.
(2) An experiment to verify Einstein's theory of relativity,
particularly the hypothesis that the presence of large masses curves
space (gravitational relativity) -- Measurements of Mercury's apparent
position, during an eclipse of the sun, were in error to a degree
consistent with Einstein's theory.
Obviously, Astronomical experiments will seem to lie half in the realm
of physics, since the theories of physics are the tools with which we
try to understand the skies.
Astronomers and physicists, please help me out here; I'm neither.
In fact, I don't even believe in neutrinos.
- Keremath, care of:
Robison
decvax!ittvax!eosp1
or: allegra!eosp1
------------------------------
Date: Thu, 29 Dec 83 15:44 EST
From: Hengst.WBST@PARC-MAXC.ARPA
Subject: Re: AIList Digest V1 #116
The flaming on the science component of computer science intrigues me
because it parallels some of the 1960's and 1970's discussion about the
science component of social science. That particular discussion, to
which Thomas Kuhn also contributed, also has not yet reached closure
which leaves me with the feeling that science might best be described as
a particular form of behavior by practitioners who possess certain
qualifications and engage in certain rituals approved by members of the
scientific tribe.
Thus, one definition of science is that it is whatever it is that
scientists do in the name of science ( a contextual and social
definition). Making coffee would not be scientific activity but reading
a professional book or entertaining colleagues with stimulating thoughts
and writings would be. From this perspective, employing the scientific
method is merely a particular form of engaging in scientific practice
without judging the outcome of that scientific practice. Relying upon
the scientific method by unlicensed practitioners would not result in
science but in lay knowledge. This means that authoritative statements
by members of scientific community are automatically given a certain
truth value. "Professor X says this", "scientific study Y demonstrates
that . . ." should all be considered as scientific statements because
they are issued as authorative statements in the name of science. This
interpretation of science discounts the role of Edward Teller as a
credible spokesman in the area of nuclear weapons policy in foreign
affairs.
The "licensing" of the practitioners derives from the formalization of
the training and education in the particular body of knowledge: eg. a
university degree is a form of license. Scientific knowledge can
differentiate itself from other forms of knowledge on the basis of
attempts (but not necesssarily success) at formalization. Physical
sciences study phenomena which lend themselves to better quantification
(they do have better metrics!) and higher levels of formalization. The
deterministic bodies of knowledge of the physical science allow for
better prediction than the heavily probabilistic bodies of knowledge of
the social science which facilitate explanation more so than prediction.
I am not sure if a lack of predictive power or lack of availability of
the scientific method (experimental design in its many flavors) makes
anyone less a scientist. The social sciences are rich in description and
insight which in my judgment compensates for a lack of hierarchical,
deductive formal knowledge.
From this point of view computer science is science if it involves
building a body of knowledge with attempts at formulating rules in some
consistent and verfiable manner by a body of trained practitioners.
Medieval alchemy also qualifies due to its apprenticeship program (rules
for admitting members) and its rules for building knowledge.
Fortunately, we have better rules now.
Acco
------------------------------
Date: Thu 29 Dec 83 23:38:18-PST
From: Ken Laws <Laws@SRI-AI.ARPA>
Reply-to: AIList-Request@SRI-AI
Subject: Philosophy of Science Discussion
I hate to put a damper on the discussion of Scientific Method,
but feel it is my duty as moderator. The discussion has been
intelligent and entertaining, but has strayed from the central
theme of this list. I welcome discussion of appropriate research
techniques for AI, but discussion of the definition and philosophy
of science should be directed to Phil-Sci@MIT-OZ. (Net.ai members
are free to discuss whatever they wish, of course, but I will
not pass further messages on this topic to the ARPANET readership.)
-- Ken Laws
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End of AIList Digest
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