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AIList Digest Volume 2 Issue 125
AIList Digest Wednesday, 26 Sep 1984 Volume 2 : Issue 125
Today's Topics:
Expert Systems - Foster Care Placements,
LISP - Franz Lisp Help,
Inductive Proof - The Heap Problem,
Machine Translation - Natural Languages as Interlinguas,
Seminars - Semantic Modulation & SOAR Intelligent System
Administrivia - Current Distribution List
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Date: Sun, 23 Sep 84 22:34 EST
From: Ed Fox <fox%vpi.csnet@csnet-relay.arpa>
Subject: Expert System for Foster Care Placements
One of my students has begun a project as described below.
We are wondering if there are any similar projects that people
would be willing to let us know about.
Many thanks, Ed Fox.
This expert system will provide assistance to social workers charged with
finding suitable substitute care placements for children who cannot continue
to live with their families. The system's rules will be based on
expert input from social workers and an analysis of a social service agency's
case records to determine the constellation of child, natural family, and
substitute caregivers' characteristics and environmental factors which have
been associated with successful placements in the past. Users will be asked
for descriptive information about the child for whom a placement is being
sought and his/her family. System output will provide the social worker with
a description(s) of substitute care settings which can be expected to suit the
needs of the particular child and contribute to a successful placement.
------------------------------
Date: 25 Sep 1984 07:59:09-EDT
From: kushnier@NADC
Subject: Help- Franz Lisp
Help!
Does anyone have a good practical guide to Franz LISP running under UNIX
on a VAX ?
Is there a way to list the LISP environment when running the interpreter or
do you have to go in and out using the Unix editors?
Can you save the LISP envirnment to an editor file while you are in LISP?
P.S. I have the Franz LISP manual, but I haven't translated it to English yet.
P.S.S I haven't even figured out what language it's written in.......
Ron Kushnier
kushnier@nadc.arpa
[I'm not sure what's possible under Berkeley Unix (if that's what you
have) since I'm using a VAX EUNICE system. Our people have rigged the
EMACS editor so that it can be called from Franz, provided that you load
and then suspend EMACS before starting up Franz. Interpreted functions
can thus be edited and newly edited functions can be run; special editor
macros facilitate this. 4.1BSD Unix lacks the interprocess mechanisms
needed to support this (LEDIT), although EMACS process windows running
Franz are possible; 4.2BSD may be more flexible.
To examine your environment while in Franz, use the pp (pretty-print)
command. You can certainly save an environment; check out the
dumplisp and savelisp commands. For a readable Franz tutorial get
Wilensky's new LISPcraft book. -- KIL]
------------------------------
Date: 19 Sep 84 14:42:49-PDT (Wed)
From: ihnp4!houxm!mhuxj!mhuxn!mhuxl!ulysses!allegra!princeton!eosp1!robison
@ Ucb-Vax.arpa
Subject: Re: Inductive proof -- the heap problem
Article-I.D.: eosp1.1131
BUT! Human beings continually reason inductively on tiny amounts
of info, often two or even one case! We have some way of monitoring
our results and taking back some iof the inductions that were wrong.
AI has to get the hang of this some day...
--- Toby Robison
------------------------------
Date: Mon, 24 Sep 84 22:28 EST
From: Sergei Nirenburg <nirenburg%umass-cs.csnet@csnet-relay.arpa>
Subject: natural languages as interlinguas for MT
Re: using a natural language as an interlingua in a machine translation
system
A natural language and an MT interlingua have different purposes and are
designed differently. An interlingua should be ambiguity-free and should
facilitate automatic reasoning about the knowledge encoded in it. A natural
language is designed to be used by truly intelligent speakers and hearers, so
that a lot of polysemy, homonymy, anaphoric phenomena, even outright errors
can be put up with -- because the understander is so sophisticated. Brevity
is at a premium in natural language communication, not clarity.
The most recent attempt to use a language designed for humans as an MT
interlingua is the Dutch researcher A. Witkam's attempt in his DLT machine
translation project. He plans to use Binary-Coded Esperanto (BCE) as the
interlingua in a planned multilingual MT system.
An analysis of the approach shows that in reality the system involves two
complete (transfer-based) translation modules: 1) Source language to BCE; and
2) BCE to Target language.
Of many points of criticism possible let me mention just that this
approach (in effect, double transfer) has nothing to do with AI methods.
If transfer is used, it is not clear why an interlingua should be involved at
all.
For some more discussion see Tucker and Nirenburg, "Machine Translation: A
Contemporary View", in the 1984 issue of the Annual Review of Information
Science and Technology.
At the same time, it would be nice to see a technical discussion of the
system by Guzman de Rojas -- is any such thing available?
Sergei
------------------------------
Date: Mon, 24 Sep 1984 15:30 EDT
From: WELD%MIT-OZ@MIT-MC.ARPA
Subject: Seminar - Semantic Modulation
[Forwarded from the MIT bboard by Laws@SRI-AI.]
The AI revolving seminar this week is by David McAllester:
SEMANTIC MODULATION: A Relevance Based Inference Technique
The Reasoning Utility Package RUP provides a set of
propositonal inference mechanisms for constructing inference-based
data bases and reasoning systems. This talk will present new
inference mechanisms which can be incorporated into the RUP
architecture. These inference mechansisms reason about quantified
formula using a technique based on the "modulation" of the
interpretation of free parameters. By modulating the interpretation
of free parameters it is possible to perform a wide variety of
quantificational inferences without ever "consing" new formulae.
The semantic modulation inference mechanism relies on a notion
of relevance in propositional reasoning: when a formula is proven
one can determine a subset of premises relevant to the proof.
The relevant subset is usually smaller than the set of premises actually
used in the proof. Semantic modulation is also closely related to
the notions of "inheritance" and "virtual copy" used in semantic networks.
Time: 2:00PM Wednesday Sept. 26 (THIS Wednesday)
Place: 7th Floor Playroom
------------------------------
Date: Tue 25 Sep 84 11:09:13-PDT
From: Paula Edmisten <Edmisten@SUMEX-AIM.ARPA>
Subject: Seminar - SOAR Intelligent System
[Forwarded from the Stanford SIGLUNCH distribution by Laws@SRI-AI.]
DATE: Friday, September 28, 1984
LOCATION: Chemistry Gazebo, between Physical and Organic Chemistry
TIME: 12:05
SPEAKER: John Laird,
Xerox Corp.
ABSTRACT: SOAR: An Architecture for General Intelligence
I will present recent progress in developing an architecture for general
intelligence, called Soar. In Soar, all problem solving occurs as
search in a problem space and all knowledge is encoded as production
rules. I will describe the Soar architecture and then present three
demonstrations of its generality and power.
1. Universal Subgoaling: All subgoals are created automatically by the
architecture whenever the problem solver is unable to carry out the
basic functions of problem solving (so that all subgoals in Soar are
also meta-goals). All the power of Soar is available in the subgoals,
including creating new subgoals, making Soar a completely reflective
problem solver.
2. A Universal Weak Method: The weak methods emerge from knowledge about
a task instead of through explicit representation and selection.
3. R1-Soar: Although Soar was designed for general problem-solving, it
is also effective in the knowledge-intensive domains of expert systems.
This will be demonstrated by a partial implementation of the R1 expert
system in Soar.
Soar also has a general learning mechanism, called Chunking. Paul
Rosenbloom will present this aspect of our work at the SIGLunch on
October 5.
------------------------------
Date: Tue 25 Sep 84 14:08:12-PDT
From: Ken Laws <Laws@SRI-AI.ARPA>
Reply-to: AIList-Request@SRI-AI
Subject: Current Distribution List
SIGART has recently been publishing names of companies involved in AI,
which started me wondering just where AIList goes. The following are
organizations that I mail to directly, as nearly as I can figure out
from the net names. In some cases the digest goes to numerous campuses,
departments, or laboratories; in others it goes to a single individual.
AIList also goes to numerous sites through indirect remailings,
particularly through Usenet redistribution. If anyone would like to
add to my list, please send a brief message to AIList-Request@SRI-AI.ARPA.
GOVERNMENT AND MILITARY:
Admiralty Surface Weapons Establishment
Air Force Institute of Technology Data Automation
Army Armament Research and Development Command
Army Aviation Systems Command
Army Communications Electronics Command
Army Engineer Topographic Laboratory
Army Materiel Systems Analysis Activity
Defense Communications Engineering Center
National Aeronautics and Space Administration
National Library of Medicine
National Research Council Board on Telecomm.-Comp. Applications
National Science Foundation
Naval Air Development Center
Naval Intelligence Processing System Support Activity
Naval Ocean Systems Center
Navel Personnel Research and Development Center
Navel Research Laboratory
Navel Surface Weapons Center
Norwegian Defence Research Establishment
LABORATORIES AND RESEARCH INSTITUTES:
Aerospace Medical Research Laboratory
Brookhaven National Laboratory
Center for Seismic Studies
Center for Studies of Language and Information
Jet Propulsion Laboratory
Lawrence Berkeley Laboratory
Lawrence Livermore Labs
Los Alamos National Laboratory
MIT Lincoln Laboratory
NASA Ames Research Center
Norwegian Telecommunication Administration Research Institute
Oak Ridge National Laboratory
Sandia National Laboratories
USC Information Sciences Institute
CORPORATIONS AND NONPROFIT ORGANIZATIONS:
ACM SIGART
Advanced Computer Communications
Advanced Information and Decision Systems
Bolt Beranek and Newman Inc.
Compion Corp.
Digital Equipment Corp.
Ford Aerospace and Communications Corp.
GTE Laboratories
General Motors Research
Hewlett-Packard Laboratories
Honeywell, Inc.
Hughes Research
IntelliGenetics
International Business Machines
Kestrel Institute
Linkabit
Litton Systems
Logicon, Inc.
Marconi Research Centre, Chelmsford
Northrop Research Center
Perceptronics
Philips
Rome Air Development Center
SRI International
Science Applications, Inc.
Software A&E
Tektronix, Inc.
Texas Instruments
The Aerospace Corporation
The MITRE Corporation
The Rand Corporation
Tymshare
Xerox Corporation
UNIVERSITIES:
Boston University
Brandeis University
Brown University
California Institute of Technology
Carnegie-Mellon University
Clemson University
Colorado State University
Columbia University
Cornell University
Georgia Institute of Technology
Grinnell College
Harvard University
Heriot_Watt University, Edinburgh
Louisiana State University
Massachusetts Institute of Technology
New Jersey Institute of Technology
New York University
Oklahoma State University
Rice University
Rochester University
Rutgers University
St. Joseph's University
Stanford University
State University of New York
University College London
University of British Columbia
University of California (Berkeley, Davis, UCF, UCI, UCLA, Santa Cruz)
University of Cambridge
University of Delaware
University of Edinburgh
University of Massachusetts
University of Michigan
University of Minnesota
University of North Carolina
University of Pennsylvania
University of South Carolina
University of Southern California
University of Tennessee
University of Texas
University of Toronto
University of Utah
University of Virginia
University of Washington
University of Wisconsin
Vanderbilt
Virginia Polytechnic Institute
Yale University
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End of AIList Digest
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