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AIList Digest Volume 2 Issue 019
AIList Digest Wednesday, 15 Feb 1984 Volume 2 : Issue 19
Today's Topics:
Requests - OPS5 & IBM LISP,
LISP - Timings,
Bindings - G. Spencer-Brown,
Knowledge Acquisition - Regrets,
Alert - 4-Color Problem,
Brain Theory - Definition,
Seminars - Analogy & Causal Reasoning & Tutorial Discourse
----------------------------------------------------------------------
Date: Mon 13 Feb 84 10:06:53-PST
From: Ted Markowitz <G.TJM@SU-SCORE.ARPA>
Subject: OPS5 query
I'd like to find out some information on acquiring a copy of
the OPS5 system. Is there a purchase price, is it free-of-charge,
etc. Please send replies to
G.TJM@SU-SCORE
Thanks.
--ted
------------------------------
Date: 1 Feb 1984 15:14:48 EST
From: Robert M. Simmons <simmons@EDN-UNIX>
Subject: lisp on ibm
Can anyone give me pointers to LISP systems that run on
IBM 370's under MVS? Direct and indirect pointers are
welcome.
Bob Simmons
simmons@edn-unix
------------------------------
Date: 11 Feb 84 17:54:24 EST
From: John <Roach@RUTGERS.ARPA>
Subject: Timings of LISPs and Machines
I dug up these timings, they are a little bit out of date but seem a little
more informative. They were done by Dick Gabriel at SU-AI in 1982 and passed
along by Chuck Hedrick at Rutgers. Some of the times have been updated to
reflect current machines by myself. These have been marked with the
date of 1984. All machines were measured using the function -
an almost Takeuchi function as defined by John McCarthy
(defun tak (x y z)
(cond ((not (< y x))
z)
(t (tak (tak (1- x) y z)
(tak (1- y) z x)
(tak (1- z) x y)))))
------------------------------------------
(tak 18. 12. 6.)
On 11/750 in Franz ordinary arith 19.9 seconds compiled
On 11/780 in Franz with (nfc)(TAKF) 15.8 seconds compiled (GJC time)
On Rutgers-20 in Interlisp/1984 13.8 seconds compiled
On 11/780 in Franz (nfc) 8.4 seconds compiled (KIM time)
On 11/780 in Franz (nfc) 8.35 seconds compiled (GJC time)
On 11/780 in Franz with (ffc)(TAKF) 7.5 seconds compiled (GJC time)
On 11/750 in PSL, generic arith 7.1 seconds compiled
On MC (KL) in MacLisp (TAKF) 5.9 seconds compiled (GJC time)
On Dolphin in InterLisp/1984 4.81 seconds compiled
On Vax 11/780 in InterLisp (load = 0) 4.24 seconds compiled
On Foonly F2 in MacLisp 4.1 seconds compiled
On Apollo (MC68000) PASCAL 3.8 seconds (extra waits?)
On 11/750 in Franz, Fixnum arith 3.6 seconds compiled
On MIT CADR in ZetaLisp 3.16 seconds compiled (GJC time)
On MIT CADR in ZetaLisp 3.1 seconds compiled (ROD time)
On MIT CADR in ZetaLisp (TAKF) 3.1 seconds compiled (GJC time)
On Apollo (MC68000) PSL SYSLISP 2.93 seconds compiled
On 11/780 in NIL (TAKF) 2.8 seconds compiled (GJC time)
On 11/780 in NIL 2.7 seconds compiled (GJC time)
On 11/750 in C 2.4 seconds
On Rutgers-20 in Interlisp/Block/84 2.225 seconds compiled
On 11/780 in Franz (ffc) 2.13 seconds compiled (KIM time)
On 11/780 (Diablo) in Franz (ffc) 2.1 seconds compiled (VRP time)
On 11/780 in Franz (ffc) 2.1 seconds compiled (GJC time)
On 68000 in C 1.9 seconds
On Utah-20 in PSL Generic arith 1.672 seconds compiled
On Dandelion in Interlisp/1984 1.65 seconds compiled
On 11/750 in PSL INUM arith 1.4 seconds compiled
On 11/780 (Diablo) in C 1.35 seconds
On 11/780 in Franz (lfc) 1.13 seconds compiled (KIM time)
On UTAH-20 in Lisp 1.6 1.1 seconds compiled
On UTAH-20 in PSL Inum arith 1.077 seconds compiled
On Rutgers-20 in Elisp 1.063 seconds compiled
On Rutgers-20 in R/UCI lisp .969 seconds compiled
On SAIL (KL) in MacLisp .832 seconds compiled
On SAIL in bummed MacLisp .795 seconds compiled
On MC (KL) in MacLisp (TAKF,dcl) .789 seconds compiled
On 68000 in machine language .7 seconds
On MC (KL) in MacLisp (dcl) .677 seconds compiled
On SAIL in bummed MacLisp (dcl) .616 seconds compiled
On SAIL (KL) in MacLisp (dcl) .564 seconds compiled
On Dorado in InterLisp Jan 1982 (tr) .53 seconds compiled
On UTAH-20 in SYSLISP arith .526 seconds compiled
On SAIL in machine language .255 seconds (wholine)
On SAIL in machine language .184 seconds (ebox-doesn't include mem)
On SCORE (2060) in machine language .162 seconds (ebox)
On S-1 Mark I in machine language .114 seconds (ebox & ibox)
I would be interested if people who had these machines/languages available
could update some of the timings. There also isn't any timings for Symbolics
or LMI.
John.
------------------------------
Date: Sun, 12 Feb 1984 01:14 EST
From: MINSKY%MIT-OZ@MIT-MC.ARPA
Subject: AIList Digest V2 #14
In regard to G Spencer Brown, if you are referring to author of
the Laws of Form, if that's what it was called: I believe he was
a friend of Bertrand Russell and that he logged out
quite a number of years ago.
------------------------------
Date: Sun, 12 Feb 84 14:18:04 EST
From: Brint <abc@brl-bmd>
Subject: Re: "You cant go home again"
I couldn't agree more (with your feelings of regret at not
capturing the expertise of the "oldster" in meterological
lore).
My dad was one of the best automotive diagnosticians in
Baltimore until his death six years ago. His uncanny
ability to pinpoint a problem's cause from external
symptoms was locally legendary. Had I known then what I'm
beginning to learn now about the promise of expert systems,
I'd have spent many happy hours "picking his brain" with
the (unfilled) promise of making us both rich!
------------------------------
Date: Mon 13 Feb 84 22:15:08-EST
From: Jonathan Intner <INTNER@COLUMBIA-20.ARPA>
Subject: The 4-Color Problem
To Whom It May Concern:
The computer proof of the 4 - color problem can be found in
Appel, K. and W. Haken ,"Every planar map is 4-colorable-1 :
Discharging", "Every planar map is 4-colorable-2: Reducibility",
Illinois Journal of Mathematics, 21, 429-567 (1977). I haven't looked
at this myself, but I understand from Mike Townsend (a Prof here at
Columbia) that the proof is a real mess and involves thousands of
special cases.
Jonathan Intner
INTNER@COLUMBIA-20.ARPA
------------------------------
Date: 11 Feb 1984 13:50-PST
From: Andy Cromarty <andy@AIDS-Unix>
Subject: Re: Brain, a parallel processor?
What are the evidences that the brain is a parallel processor?
My own introspection seem to indicate that mine is doing time-sharing.
-- Rene Bach <BACH@SUMEX-AIM.ARPA>
You are confusing "brain" with "mind".
------------------------------
Date: 10 Feb 1984 15:23 EST (Fri)
From: "Daniel S. Weld" <WELD%MIT-OZ@MIT-MC.ARPA>
Subject: Revolving Seminar
[Forwarded by SASW@MIT-MC.]
Wednesday, February 15, 4:00pm 8th floor playroom
Structure-Mapping: A Theoretical Framework for Analogy
Dedre Gentner
The structure-mapping theory of analogy describes a set of
principles by which the interpretation of an analogy is derived
from the meanings of its terms. These principles are
characterized as implicit rules for mapping knowledge about a
base domain into a target domain. Two important features of the
theory are (1) the rules depend only on syntactic properties of
the knowledge representation, and not on the specific content of
the domains; and (2) the theoretical framework allows analogies
to be distinguished cleanly from literal similarity statements,
applications of general laws, and other kinds of comparisons.
Two mapping principles are described: (1) Relations between
objects, rather than attributes of objects, are mapped from base
to target; and (2) The particular relations mapped are determined
by @u(systematicity), as defined by the existence of higher-order
relations. Psychological experiments supporting the theory are
described, and implications for theories of learning are
discussed.
COMING SOON: Tomas Lozano-Perez, Jerry Barber, Dan Carnese, Bob Berwick, ...
------------------------------
Date: Mon 13 Feb 84 09:15:36-PST
From: Juanita Mullen <MULLEN@SUMEX-AIM.ARPA>
Subject: SIGLUNCH ANNOUNCEMENT - FEBRUARY 24, 1984
[Reprinted from the Stanford SIGLUNCH distribution.]
Friday, February 24, 1984
LOCATION: Chemistry Gazebo, between Physical & Organic Chemistry
12:05
SPEAKER: Ben Kuipers, Department of Mathematics
Tufts University
TOPIC: Studying Experts to Learn About Qualitative
Causal Reasoning
By analyzing a verbatim protocol of an expert's explanation we can
derive constraints on the conceptual framework used by human experts
for causal reasoning in medicine. We use these constraints, along
with textbook descriptions of physiological mechanisms and the
computational requirements of successful performance, to propose a
model of qualitative causal reasoning. One important design decision
in the model is the selection of the "envisionment" version of causal
reasoning rather than a version based on "causal links." The
envisionment process performs a qualitative simulation, starting with
a description of the structure of a mechanism and predicting its
behavior. The qualitative causal reasoning algorithm is a step toward
second-generation medical diagnosis programs that understand how the
mechanisms of the body work. The protocol analysis method is a
knowledge acquisition technique for determining the conceptual
framework of new types of knowledge in an expert system, prior to
acquiring large amounts of domain-specific knowledge. The qualitative
causal reasoning algorithm has been implemented and tested on medical
and non-medical examples. It will be the core of RENAL, a new expert
system for diagnosis in nephrology, that we are now developing.
------------------------------
Date: 12 Feb 84 0943 EST (Sunday)
From: Alan.Lesgold@CMU-CS-A (N981AL60)
Subject: colloquium announcement
[Forwarded from the CMU-C bboard by Laws@SRI-AI.]
THE INTELLIGENT TUTORING SYSTEM GROUP
LEARNING RESEARCH AND DEVELOPMENT CENTER
UNIVERSITY OF PITTSBURGH
AN ARCHITECTURE FOR
TUTORIAL DISCOURSE
BEVERLY P. WOOLF
COMPUTER AND INFORMATION SCIENCE DEPARTMENT
UNIVERSITY OF MASSACHUSETTS
WEDNESDAY, FEBRUARY 15,
2:00 - 3:00, LRDC AUDITORIUM (SECOND FLOOR)
Human discourse is quite complex compared to the present ability of
machines to handle communication. Sophisticated research into discourse
is needed before we can construct intelligent interactive systems. This
talk presents recent research in the areas of discourse generation, with
emphasis on teaching and tutoring dialogues.
This talk describes MENO, a system where hand tailored rules have
been used to generate flexible responses in the face of student
failures. The system demonstrates the effectiveness of separating
tutoring knowledge and tutoring decisions from domain and student
knowledge. The design of the system suggests a machine theory of
tutoring and uncovers some of the conventions and intuitions of tutoring
discourse. This research is applicable to any intelligent interface
which must reason about the users knowledge.
------------------------------
End of AIList Digest
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