LAWS@SRI-AI.ARPA (12/31/84)
From: AIList Moderator Kenneth Laws <AIList-REQUEST@SRI-AI.ARPA>
AIList Digest Monday, 31 Dec 1984 Volume 2 : Issue 183
Today's Topics:
Projects - Cognitive Science Dictionary,
AI Tools - Cheap Lisp Machines & Xerox,
News - Recent Articles & Thinking Machines Corporation & Space Shuttle,
Courses - Massively Parallel Models of Intelligence (CMU) &
Reasoning about the Physical World (UIUC)
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Date: Sun, 30 Dec 84 21:53:17 est
From: 20568%vax1@cc.delaware (FRAWLEY)
Subject: Cognitive Science Dictionary
I recently spoke with a publisher about the possibility of compiling
a Dictionary of Cognitive Science. I'm sending out this preliminary
inquiry to you all to see what you think of the idea. I'd appreciate
responses to any or all of the following:
1. Is the idea of such a dictionary good, bad, ridiculous...?
2. Is such a dictionary a feasible project?
3. If the project is feasible, what areas of Cognitive Science
ought to be covered?
4. What do you think of the marketability of such a dictionary?
5. If the project is feasible, what form should the dictionary take
(i.e., standard dictionary form, encyclopedic form, etc.)?
You can send your responses via the AIList or to me directly.
Thanks,
Bill Frawley
Linguistics
U. of Delaware
20568.ccvax1@udel
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Date: Thu, 27 Dec 84 17:07:33 pst
From: hplabs!sdcrdcf!darrelj@Berkeley (Darrel VanBuer)
Subject: A Very Cheap Lisp Machine
To be slightly partisan toward the machines I
use, Xerox Dandelions can be had for under $19,000 in some configurations.
For not much over the high end of the proposal in V2 #182, you GET the high
end machine (except addition of Ethernet and a display with 6 times the
pixels of the Macintosh). About a third of the cost of a Dandelion is for
the Interlisp software (inferred from the unbundled Star price list).
This is a reasonable cost given the complexity of a full-blown display-oriented
Lisp environment and the (relatively) small market for Lisp machines.
Darrel J. Van Buer, PhD
System Development Corp.
2500 Colorado Ave
Santa Monica, CA 90406
(213)820-4111 x5449
...{allegra,burdvax,cbosgd,hplabs,ihnp4,orstcs,sdcsvax,ucla-cs,akgua}
!sdcrdcf!darrelj
VANBUER@USC-ECL.ARPA
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Date: 26 Dec 1984 1757 PST
From: Larry Carroll <LARRY@JPL-VLSI.ARPA>
Reply-to: LARRY@JPL-VLSI.ARPA
Subject: Xerox AI
Paul Erler's message reminds me: the latest Computerworld has a full-page
ad with the banner XEROX ANNOUNCES A 15-YEAR HEADSTART IN ARTIFICIAL
INTELLIGENCE. It seems they're now selling and supporting what they call
the Xerox AI System. It includes a combination of 1108 or 1132 workstations,
Interlisp D and LOOPS, and training as well as support. Added info can be
gotten from
attn: AI Marketing, MS 1245
Xerox Special Information Systems
Artificial Intelligence Business Unit
250 N. Halstead St., PO Box 7018
Pasadena, CA 91109
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Date: Sat, 29 Dec 84 06:24:05 cst
From: Laurence Leff <leff%smu.csnet@csnet-relay.arpa>
Subject: Recent AI Articles
New Scientist November 8, 1984 Volume 104 No. 1429 pp 10
Japan unveils its fifth generation
New Scientist November 15, 1984 Volume 104 No. 1430
AI is Stark Naked from the Ankles Up. [An entertaining article
claiming that the emperor's new clothes (AI) consist only of
sneakers (20-year-old expert systems technology). -- KIL]
Distributing Computing
APIC studies in Data Processing Volume 20
Edited by F. B. Chambers D. A. Dune G. P. Jones
Academic Press $22.50
The following titles in this compendium might be of interest:
Using Algebra for Concurrency
Reasoning about Concurrent Systems
Functional Programming
Logic Programming and Prolog
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Date: Mon 31 Dec 84 11:40:57-PST
From: Ken Laws <Laws@SRI-AI.ARPA>
Subject: Thinking Machines Corporation
From the January, 1985, issue of Omni, p. 33, by Edward Rosenfeld:
[...]
The latest fusion of acadame and venture capital is Thinking
Machines Corporation (TMC), a Cambridge, Mass., company that
boasts Marvin Minsky, cofounder of MIT's AI Laboratory and one
of the pioneers of AI, as one of its founders.
A group of investors headed by CBS founder William Paley has
reportedly put up a $10 million stake to get TMC off the ground.
AI insiders refer to the company as the Marv and Marv Show because,
in addition to Marvin Minsky, TMC has also acquired the services
of Marvin Denicoff, who formerly guided the AI programs at the
Office of Naval Research.
The company's first product, currently in prototype development,
will be the connection machine, a parallel-processing supercomputer
designed by W. Daniel Hillis, of MIT. [...]
-- Ken Laws
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Date: Fri, 28 Dec 84 14:04:56 est
From: nikhil@mit-fla (Rishiyur S. Nikhil)
Subject: AI and the Shuttle
Here are some items of interest from Aviation Week and Space Technology:
++++ AWST Sep 17, 1984, page 79
Johnson Space Center (Houston) officials expect to use AI techniques in
future Shuttle missions, beginning late 1984 or in 1985.
The first use will be Navex, a "navigational expert system". Currently, the
navigation console position is manned in 4 shifts, with 3 controllers per
shift. Each person needs 2 years of training to make high-speed decisions
about shuttle velocity and trajectory.
JSC officials expect to man it with one controller per shift in conjunction
with Navex.
Navex is built on ART (Automatic Reasoning Tool), which is written in Lisp.
ART is a product of Inference Corp. of Los Angeles. Navex was developed by
Inference Corp. and LinCom Corp. of Houston.
++++ AWST Dec10, 1984, page 24
NASA will test Navex along with its human counterparts in Jan 1985. A Symbolics
computer will run in a lab near Mission Control at Johnson Space Center,
Houston, and will be wired to the navigator console position. They expect
it to make decisions about Shuttle velocity and trajectory six times faster
than humans.
By March, an AI program will perform Shuttle electrical system checks during
pre-launch ground preparations. The actual program is finished, but
documentation to explain it will take 3 months. (!!)
By late summer 1985, Johnson Space Center wil complete an expert system
that captures the expertise of a person whose job would be to talk the
shuttle down during re-entry if it were to emerge from a radio blackout
with malfunctioning navigation instruments. It will take 2 months to build,
and will run in Mission Control as an advisor to flight controllers.
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Date: 22 Dec 1984 1152-EST
From: Geoff Hinton <HINTON@CMU-CS-C.ARPA>
Subject: Course - Massively Parallel Models of Intelligence
[Forwarded from the CMU bboard by Laws@SRI-AI.]
Advanced Course on:
MASSIVELY PARALLEL MODELS OF NATURAL INTELLIGENCE
Geoffrey Hinton & Scott Fahlman
This is a 7 week advanced course. It meets from 11.30 - 12.50 on Wednesdays
and Fridays in 5409, starting on Wednesday Jan 16. A reading list and a brief
description of each lecture will be available from Geoff Hinton on Jan 15th.
The course covers models of @b(search, representation,) and @b(learning) in
networks of simple processing elements that are richly interconnected. The
emphasis will be on the computational properties of these networks, but we will
also cover the psychological and neurophysiological evidence for and against
various models.
SEARCH
The main search technique used in these networks is iterative relaxation.
Five different models of relaxation will be presented and their performance
will be compared on a variety of tasks including stereo-fusion,
surface-interpolation, shape-recognition, and figure-ground segmentation.
Other search methods will also be covered.
REPRESENTATION
To make efficient use of the representational capacity of massively parallel
networks, it is often necessary to use novel kinds of representation in which
individual processing elements do not have a simple relationship to the
concepts being represented. We will cover methods of representing continuous
variables, high-dimensional feature spaces, spatial transformations, simple
associations, schemas, trees, production systems, and Clyde. We will discuss
the interaction between representational efficiency and ease of search for each
kind of representation.
LEARNING
We will cover the history of attempts to make networks that learn by modifying
connection strengths, and show why these attempts generally failed or worked
only for very circumscribed domains. The difficult problem in learning is to
construct @i(new) representations. We will compare three different models that
create representations by modifying connection strengths. We will also compare
these connectionist models with more conventional AI learning methods.
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Date: Thu, 27 Dec 84 20:57:01 cst
From: Kenneth Forbus <forbus%uiucdcsp@uiuc.ARPA>
Subject: Course - Reasoning about the Physical World (UIUC)
Course Announcement - U. of Illinois at Urbana
CS 497, Spring 1985
Title: Reasoning about the Physical World
Instructor: Ken Forbus
This graduate seminar will examine principles and methods developed in
Artificial Intelligence for reasoning about problems involving space, time,
processes, and action. Topics include: solving word problems; qualitative
physics; planning actions, experiments, assemblies, and routes; analysis,
design, troubleshooting, and control of engineered systems. A solid AI
background will be assumed.
Outline:
1. Solving Textbook Physics Problems
Survey of programs: Charniak's CARPS, Novak, Larkin,
Bundy, de Kleer.
Transformation from natural language to equations
Symbolic algebra
2. Qualitative Physics
Qualitative State representation: ontology,
making predictions, correlating qualitative
results with quantitative results, using
qualitative reasoning to guide search for
quantitative solutions.
Qualitative Process theory: processes as mechanisms of
change, influences as representation of equations,
basic deductions sanctioned by QP theory, prediction,
measurement interpretation.
Qualitative System Dynamics: breakdown of processes when
system connectivity becomes high, device-centered
model for physics. Confluences as representation of
equations, constraint-satisfaction and propagation
techniques for solving confluences.
3. Planning
"Classical" AI planning: GPS, STRIPS, NOAH, MOLGEN. Limitations
due to inadequate models of time, space, and action.
Modelling time: Histories and Chronicles. Allen's interval-based
formulation. Vere's DEVISER. Theories of action.
Modelling space: symbolic, metric, and analog representations
of space. The "visual routines" model of human spatial
competence.
Robot planning (routes): Configuration space approach and related
computational problems. Quantizing free space into
"freeways".
Robot planning (assembly): Symbolic analysis of errors. Automatic
insertion of inspection steps into assembly plans.
4. Engineering Problem Solving
Analysis: Propagation of constraints, EL. Qualitative
analysis for functional recognition.
Design: SYN, the role of causality in circuit design,
circuit grammars.
Troubleshooting: Digital electronics: Davis' group and the DART
project. Continuous systems: SOPHIE.
Control: Temporal logic for synthesizing control strategies.
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End of AIList Digest
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