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AIList Digest Volume 2 Issue 118
AIList Digest Friday, 14 Sep 1984 Volume 2 : Issue 118
Today's Topics:
AI Tools - MACSYMA Copyright,
Philosophy - The Nature of Proof,
Robotics - Brian Reid's Robot Cook,
Humor - Self-Reference & Seminar on Types in Lunches,
Journals - Sigart Issue on Applications of AI in Engineering
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Date: 10 September 1984 15:29-EDT
From: Paula A. Vancini <PAULA @ MIT-MC>
Subject: MACSYMA Notice
[Forwarded from the MIT bboard by Laws@SRI-AI.]
TO: ALL MACSYMA USERS
FROM: MIT Patent and Copyright Office
DATE: August 31, 1984
SUBJECT: Recent Notices by Paradigm Associates Regarding MACSYMA Software
Please be advised that the version of MACSYMA designated by Paradigm
Associates in recent messages over this network as "DOE MACSYMA" is a
version of MACSYMA copyrighted to MIT. "DOE MACSYMA" is an improper
designation. MIT has delivered a copy of the MIT MACSYMA software to
DOE, pursuant to MIT's contraactual obligations to DOE.
Also be advised that Symbolics, Inc. is the only commercial company
authorized by MIT to perform maintenance services on, or to make
enhancements to, the MIT copyrighted versions of MACSYMA.
MIT hereby disclaims any association with Paradigm Associates and has
not granted Paradigm licensing rights to commercially make use of its
copyrighted versions of the MACSYMA or NIL software.
Queries to Hynes%MIT-XX@MIT-MC
------------------------------
Date: 10 Sep 84 14:33:25-PDT (Mon)
From: decvax!genrad!teddy!rmc @ Ucb-Vax.arpa
Subject: Re: Now and Then
Article-I.D.: teddy.403
I am not sure I agree that an inductive proof proves any more
or less than a deductive proof. The basis of induction is to claim
1) I have applied a predicate to some specific cases within a large
set (class) of cases.
2) I detect a pattern in the result of the predicate over those cases
3) I predict that the results of the predicate will continue following
the pattern for the rest of the cases in the set.
I state the proof pattern this way to include inductive arguments about
natural world phenomena as well as mathematical induction.
The proof is valid if the accepted community of experts agrees that the
proof is valid (see for example various Wittgenstein and Putname essays
on the foundations of mathematics and logic). The experts could be
wrong for a variety of reasons. Natural law could change. The
argument may be so complicated that everyone gets lost and misses a
mistake (this has even happened before!) The class of cases may be
poorly chosen. etc.
The disagreement seems to be centered around a question of
whether this community of experts accepts causality as part of the
model. If it is, then we can use causality as an axiom in our proof
systems. But it still boils down to what the experts accept.
R Mark Chilenskas
decvax!genrad!teddy!rmc
------------------------------
Date: 11 Sep 84 9:27:15-PDT (Tue)
From: ihnp4!houxm!mhuxl!ulysses!allegra!princeton!eosp1!robison @
Ucb-Vax.arpa
Subject: Re: Now and Then
Article-I.D.: eosp1.1106
Mark Chilenskas discussion of inductive proof is not correct for
mathematics, and greatly understates the strength of
mathematical inductive proofs. These work as follows:
Given a hypothesis;
- Prove that it is true for at least one case.
- Then prove that IF IT IS TRUE FOR A GENERIC CASE,
IT MUST BE TRUE FOR THE NEXT GENERIC CASE.
For example, in a hypothesis about an expression with regard
to all natural numbers, we might show that it is true if "n=1".
We then show that IF it is true for "n", it is true for "n+1".
By induction we have shown that the hypothesis is absolutely true
for every natural number. Since true: n=1 => true for n=2,
true: n=2 => true for n=3, etc.
It is the responsibility of the prover to prove that induction
through all generic cases is proper; when it is not, additional
specific cases must be proved, or induction may not apply at all.
Such an inductive proof is absolutely true for the logical system it
is defined in, and just as correct as any deductive proof.
When our perception of the natural laws change, etc., the proof
remains true, but its usefulness may become nil if we perceive
that no system in the real world could possibly correspond to the proof.
In non-mathematical systems, it is possible that both deductive
and inductive proofs will be seriously flawed, and I doubt one
can try to prefer "approximate proofs" of one type over the other.
If a system is not well-enough defined to permit accurate logical
reasoning, then the chances are that an ingenious person can
prove anything (see net.flame and net.religion for examples, also
the congressional record).
- Toby Robison (not Robinson!)
allegra!eosp1!robison
or: decvax!ittvax!eosp1!robison
or (emergency): princeton!eosp1!robison
------------------------------
Date: Thu 13 Sep 84 09:14:34-PDT
From: Ken Laws <Laws@SRI-AI.ARPA>
Subject: Inductive Proof - The Heap Problem
As an example of improper induction, consider the heap problem.
A "heap" of one speck (e.g., of flour) is definitely a small heap.
If you add one speck to a small heap, you still have a small heap.
Therefore all heaps are small heaps.
-- Ken Laws
------------------------------
Date: Fri 7 Sep 84 09:40:42-CDT
From: Aaron Temin <CS.Temin@UTEXAS-20.ARPA>
Subject: Robot chef bites off too much
[Forwarded from the UTexas-20 bboard by Laws@SRI-AI.]
Our West Coast correspondent has returned with (among other things) an
article from the San Jose Mercury News entitled "Robot cooks if it finds
the beef" about Brian Reid's(TM){1} attempts to program a robot to cook beef
Wellington. [...]
Aaron
{1} Brian Reid is a trademark of ScribeInc., Ouagadougou, Bourkina Fasso.
[I have copied the following excerpts from the June 10 article. -- KIL]
Robot cooks if it finds the beef
by Kathy Holub
Some professors will do anything for a theoretical exercise. Brian
K. Reid, a food-loving assistant professor of electrical engineering
at Stanford University, recently tried to simulate teaching a mindless
robot how to cook beef Wellington, using Julia Child's 16-page recipe.
He failed. Try telling a robot what "spread seasoning evenly" means.
"You have to specify the number of grams (of seasoning) per square
centimeter," he said, with a wry smile.
It took him 13 hours and 60 pages of computer instructions just to
teach the would-be automaton how to slice and season a slab of beef
and put it safely in the oven. Julia Child takes only three pages to
explain these simple steps. "Where I bogged down -- where I gave it
all up and decided to go to bed -- was when I had to tell the robot
how to wrap the beef in pastry," he said.
But Reid, an excellent cook with a doctorate in computer science,
was thrilled with the experiment, which involved only the computer
program and not an actual robot. "It was exactly what I wanted," he
said. "It showed that a cookbook does not tell the whole story, that
there is a lot of information missing from the recipe" that human
cooks provide without knowing it. The Wellington exercise, he
believes, will help him reach his real goal: to teach a computer how
to make integrated circuits with a computer "recipe" that doesn't
depend on human judgement, memory or common sense.
[...]
He picked the recipe for his experiment, because it's the longest
one in the book, involving 26 ingredients. Beef Wellington is a long
piece of tenderlion that is baked twice, the second time in a light
pastry crust that should turn golden brown. Forget telling the robot
what "golden brown" means.
"Every time I turned around I discovered massive numbers of
things I was contributing without even thinking about it."
For example, "Julia Child has, 'you slice the beef and season
each piece separately'" before cooking, he said. "The meat must
be cold or it won't hold its shape, but Julia doesn't tell you
that. She assumes you know."
For purposes of simplicity, Reid let the robot skip the slicing of
mushrooms and onions and sauteeing them in butter "until done."
"Cooking until done requires a great deal of knowledge. A robot
doesn't know that fire [in the pan] isn't part of the process. It
would happily burn the pan."
But just telling the robot how to slice the meat, season it,
reassemble it with skewers and put it in the oven was tricky enough --
like teaching a 3-year-old to fix a car. "You can't just say, 'Cut
into slices,' Reid said. "You have to say, 'Move knife one centimeter
to the east, cut.' And that assumes a sub-program telling th robot
what 'cut' means." You can't tell a robot to slice 'across.' "Across
what?" said Reid. "You can't tell a robot to eyeball something. You
have to tell it to define the center of gravity of the beef, find the
major axis of the beef and cut perpendicular to it." You also have to
tell the robot how to find the beef, that is, distinguish it from the
other ingredients, and when to stop slicing. These are standard
problems in robotics.
Other problems are not so standard. Reid forgot to specify that the
skewers should be removed before the pastry shell is added. Julia may
be forgiven for leaving this step out, but the robot trainer has
tougher work.
------------------------------
Date: 9 September 1984 04:04-EDT
From: Steven A. Swernofsky <SASW @ MIT-MC>
Subject: Humor in A.I.?
I saw the following button at a science fiction convention:
Q. Why did Douglas Hofstadter cross the road?
A. To make this riddle possible.
-- Steve
------------------------------
Date: 11 Sep 1984 14:52 EDT (Tue)
From: Walter Hamscher <WALTER%MIT-OZ@MIT-MC.ARPA>
Subject: Humor - Seminar on Types in Lunches
[Forwarded from the MIT bboard by SASW@MIT-MC.]
GENERALIZED TYPES IN GRADUATE STUDENT LUNCHES
FIRST MEETING: Friday, Sept. 14, 1984, 12:00 noon
PLACE: MIT AI Lab Playroom, 545 Tech. Sq., Cambridge, MA, USA
ORGANIZER: Walter Hamscher, (walter@oz)
An eating seminar about generalized cold cuts and spread-recognition;
gluttonism, leftovers, and indigestion; related notions appearing
in current and proposed lunches, such as volunteers, menus, and
The Roosevelt Paradox ("There is no such thing as a free lunch")
will be discussed. The slant will be toward identifying the
underlying digestional problems raised by the desired menu features.
For the first five minutes (during the visit of Prof. Gustav Fleischbrot,
Univ. of Essen) we will present and discuss the papers below starting
with the first two and concluding with the final two:
1. Burger, Chip N., ``The Nutritional Value of Pixels'',
PROC. INT'L. CONF. 5TH GENERATION INGESTION SYSTEMS, Tokyo, to
appear. Manuscript from Dept. of Computer Science, Univ. of Sandwich, 1984.
2. Burger, Chip N. and Gelly Muffin, ``A Kernel language for abstract
feta cheese and noodles'', SEMANTICS OF FETA CHEESE: PROCEEDINGS, (eds.)
Cream, MacFried and Potstick, Springer-Verlag, Lect. Notes in Comp. Sci.
173, 1-50, 1984.
3. MacDonald, Ronald, ``Noodles for standard ML'', ACM SYMP. ON LINGUICA
AND LINGUINI, 1984.
4. Munchem, J. C., ``Lamb, D-Calories, Noodles, and Ripe Fruit'',
Ph.D. Thesis, MIT, Dept. of EECS, September, 1984.
Meeting time for the first five minutes is Fri. 12:00-12:05, and
Friday 12:00-12:05 thereafter. Aerobics course credit can be arranged.
------------------------------
Date: Wednesday, 5 September 1984 23:28:30 EDT
From: Duvvuru.Sriram@cmu-ri-cive.arpa
Subject: Special Sigart Issue on Applications of AI in Engineering
SPECIAL ISSUE ON APPLICATIONS OF
AI IN ENGINEERING
The April 1985 issue of the SIGART newsletter (tentative schedule) will focus
on the applications of AI in engineering. The purpose of this issue is to
provide an overview of research being conducted in this area around the world.
The following topics are suggested:
- Knowledge-based expert systems
- Intelligent computer tutors
- Representation of engineering problems
- Natural language and graphical interfaces
- Interfacing engineering databases with expert systems
The above topics are by no means exhaustive; other related topics are welcome.
Individuals or groups conducting research in this area and who would like to
share their ideas are invited to send two copies of 3 to 4 page summaries of
their work, preferably ongoing research, before December 1, 1984. The
summaries should include a title, the names of people associated with the
research, affiliations, and bibliographical references. Since the primary aim
of this special issue is to provide information about ongoing and proposed
research, please be as brief as possible and avoid lengthy implementation
details. Submissions should be sent to D. Sriram (or R. Joobbani) at the
following address or through Arpanet to Sriram@CMU-RI-CIVE.
D. Sriram
Design Research Center
Carnegie-Mellon University
Pittsburgh, PA 15213
Tel. No. (412)578-3603
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End of AIList Digest
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