Subject: From: AIList Moderator Kenneth Laws <AIList-REQUEST%sri-ai.arpa@CSNET-RELAY>

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Date: Tue, 15 Oct 85 06:48:39 edt

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Subject: AIList Digest   V3 #147
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AIList Digest            Tuesday, 15 Oct 1985     Volume 3 : Issue 147

Today's Topics:
  Reviews - Canadian Artificial Intelligence 4 and 5 &
    Spatial Data Handling and Graphics Interface Conferences,
  Seminars - Intelligent Electronic Mail (MIT) &
    Connectionist Learning (GTE) &
    Connectionist Learning (SU) &
    Probabilistic Interpretation of Certainty Factors (SU)

----------------------------------------------------------------------

Date: 12 Oct 1985 10:59-CST
From: [email-masked]
Subject: Canadian Artificial Intelligence 4

Summary of Canadian Artificial Intelligence 4, June 1985

Should CSCSI/SCEIO Attempt to Influence National Policy
(discusses whether their organization should work to influence national
policy on AI within the Canadian government.)

AI Research Spending and politics

Discusses Canadian research spending and talent shortage.  The Canadian
AI group has been active in opposition to Star Wars.

Discussion of an episode of Magnum P. I. which featured an AI researcher
who developed some formula that would tip the balance in favor of
whoever had it.  The formula was "3 bracket prompt semicolon."

The Canadian National Research Council is starting an inventory of
Canadian robotics research

Announcement of Babbage and Lovelace's BASIC package to expand other
BASIC programs to do natural language parsing.  Runs on an IBM PC
with 64 to 96 K.

French Article on AI and Cognitive Science at the University of Montreal

Canadian Companies
Applied AI Systems, Kanata Ontario doing consulting, marketing of
software, custom software.

Review of LOGICware which sells MPROLOG.

Book reviews of "The Comercial Application of Expert System Technology"
"Artificial Intelligence: Bibliographic Summaries of the Select
Literature Volume I", "L'Intelligence Artificielle: promesses et
Realities"

Humorous Article: A Brief Review of Ignorance Engineering

Simon Fraser University AI tech report list

------------------------------

Date: 12 Oct 1985 11:44-CST
From: [email-masked]
Subject: Canadian Artificial Intelligence 5

Summary of Canadian Artificial Intelligence 5, September 1985

"Canada Prominent in IJCAI Awards" Reports on AIers prominent at
IJCAI: Levesque (Computers and Thought), Best Paper to Fagin and
Halpern and Professor Randy Goebel who stumped the band in
a taping of Mr. Carson's televison show, The Tonight Show.

"NSERC Proposes Major Increase in Research Funding" NSERC
is the Canadian science funding organization

Michel Pilot has started his own AI consulting service

Coast Mountain Intelligence specializes in Resource
Applications.  They completed an expert system on the choice
of statistical packages for geophysical data.  They are working
on expert systems for forest management and interpretation of
snow profiles for avalanche predictions

Xerox Announces Low Cost Workstations

Workshop Report: Theoretical Approaches to Natural Language
         Understanding

Workshop Report: Workshop on the Foundations of Adaptive Information
         Processing

Canada Conquers Los Angeles:  mentions Canadians prominent at IJCAI-85

Directory of Candian AI businesses

Reviews of:

Human Foundations of Advanced Computing Technology:
The Guide to the Select Literature from the Report Store

Readings in Knowledge Representation

Introduction to Artificial Intelligence by Eugene Charniak and Drew McDermott

Artificial Intelligence Applications for Business Management
Artificial Intelligence Applications for Manufacturing

Obituaries for Jeffrey Robert Sampson, Daniel Louis Shalom Berlin
         Donald Grant Kuehner and David Julian Meredith Davies

Tech Report Lists from
University of Calgary, University of Montreal, University of Toronto,
McGill University, University of Alberta, Simon Fraser University

------------------------------

Date: 14 Oct 1985 12:32-CST
From: [email-masked]
Subject: AI at conferences

International Symposium on Spatial Data Handling at the University
of Zurich, August 1984

Order from Symposium Secretariat, Department of Geography,
University of Zurich, Winterthurerstrasse 190 CH-8057 Zurich,
         Switzerland.  Price 30 dollars

Data Structures for a Knowledge-Based Geographic Information System
D. J. Perquet

Symbolic Feature Analysis and Expert Systems
B. Palmer

Autonap -- An Expert System for Automatic Map Name Placement
H. Freemand, J. Ahn

Knowledge Based Control of Search and Learning in a Large Scale GIS
T. R. Smith M. Pazner

____________________________________________________________________________

Graphics Interface 85, 11th Conference of
Candian Man-Computer Society  Montreal May 27-31

Robotics and CAD/CAM Section

Non-rigid Motion
A. R. Dill and M. D. Levine
McGill University

Electronic Assembly by Robots
C. Michaud, A. S. Malowany, M. D. Levine
McGill University

Lo Cost Geometric Modelling System for CAM
W. G. Ngai and Y. K . Chan
Chinese Univeristy of Hong Kong

Panel- Computer Graphics in Environmental Design Artificial Intelligence

Generative Design in Architecture Using an Expert System
E. Chang University of Victoria

Knowledge Engineering Application in Image Processing
K. Mikame, N. Sueda, A. Hoshi, S. Ohoniden Toshiba, Japan

Visual Perception
L. Scholl
Laura Scholl and Associates USA

------------------------------

Date: Fri, 11 Oct 85 13:49 EDT
From: [email-masked]
Subject: Seminar - Intelligent Electronic Mail (MIT)

           [Forwarded from the MIT bboard by SASW@MIT-MC.]

              Massachusetts Institute of Technology
                      Communications Forum

             Making Electronic Mail More Intelligent

                        October 31, 1985

                       Thomas Malone, MIT
          Kenneth Mayers, Digital Equipment Corporation

     Electronic messaging has become a familiar feature of the
office environment and a key element in office automation
strategy for many organizations.  As these systems spread, many
issues must be dealt with, such as accomodating evolving user
requirements, responding to rapid expansion, controlling junk
mail, and incorporating alternative technologies.  One of the
central challenges is how to enhance messaging features so that
users are not swamped by information overload.
     This forum will present the experience of Digital Equipment
Corporation, one of the pioneering users of electronic mail, and
will describe some recent innovative research at MIT which uses
artificial intelligence technology to improve the user's ability
to sort incoming messages by relevance and urgency and to route
outgoing communications to the most appropriate people within the
organizations.

                           4:00 - 6:00
               Bartos Theater for the Moving Image
                      The Wiesner Building
             (Center for Arts and Media Technology)
                   (Building E15 Lower Level)
                         20 Ames Street
              Massachusetts Institute of Technology
                    Cambridge, Massachusetts

           For further information call 617-253-3144.

------------------------------

Date: Mon, 14 Oct 85 11:31:01 EDT
From: Bernard Silver <[email-masked]>
Subject: Seminar - Connectionist Learning (GTE)

                        GTE LABORATORIES INC
                        MACHINE LEARNING SEMINAR

TITLE:          Learning by Statistical Cooperation
                      in Connectionist Networks

SPEAKER:                Prof. Andrew G. Barto
                University of Massachusetts at Amherst

TIME:                   2pm, Wednesday, October 23

PLACE:                  GTE Laboratories Inc
                        40 Sylvan Rd
                        Waltham MA 02254

Since the usual approaches to cooperative computation in networks of
neuron-like computating elements do not assume that network components
have any ``preferences," they do not make substantive contact with game
theoretic concepts, despite their use of some of the same terminology.
In the approach I describe, however, each network component, or adaptive
element, is a self-interested agent that prefers some inputs over others
and ``works" toward obtaining the most highly preferred inputs.  I
describe some of our work with an adaptive element that is robust enough
to learn to cooperate with other elements like itself in order to
further its self-interests.  It is argued that some of the long-standing
problems concerning adaptation and learning by networks might be
solvable by this form of cooperativity, and computer simulation
experiments are described that show how networks of self-interested
components that are sufficiently robust can solve rather difficult
learning problems.  A secondary aim of this talk is to suggest that
beyond what I explicitly illustrate, there is a wealth of ideas from
game theory and allied disciplines such as mathematical economics that
can be of use in thinking about cooperative computation in both nervous
systems and man-made systems.

For more information contact Bernard Silver (617) 466-2663

------------------------------

Date: Mon, 14 Oct 85 05:45:36 pdt
From: gluck@SU-PSYCH (Mark Gluck)
Subject: Seminar - Connectionist Learning (SU)

       THE COMPUTATION, COGNITION, & NEUROSCIENCE JOURNAL CLUB
                   AND SPORADIC SEMINAR SERIES

                           presents:

       "From Classical Conditioning to Cognitive Computations"

                         Richard S. Sutton
                GTE Fundamental Research Laboratory

Date: Mon, Oct. 28       Time: 12:00-1:15      Place: Room 100, Jordan Hall

One attractive aspect of connectionist models is their ability to make
contact with a wide range of fields from neuroscience to cognitive
science and AI.  In this talk I will review the status of the
"connectionist connection" between such fields and present some of my
own work as an example of a case in which pursuing it has been fruitful.
I will present a sequence of three closely-related connectionist
learning models.  The first was presented by Sutton and Barto in 1981 as
a real-time model of classical conditioning consistent with many
behavioral phenomena including blocking, conditioned inhibition, the ISI
dependency, higher-order conditioning, and serial-compound effects.  The
second model is the result of changes made to the first in trying to use
it in an AI learning problem.  Remarkably, the modified model is not
only very effective on the AI problem, but is also a better match to the
classical conditioning data than the first model.  I am currently
working on a third model that is able to reproduce the
animal learning phenomena of latent learning and sensory
preconditioning.  The AI goal for this model is to build a system that
can learn about and then reason about its environment.  The model shows
promise of being simultaneously very successful as all of 1) a model of
classical conditioning, 2) an aid to AI machine learning systems, and 3)
a model of simple forms of inference and planning.

*****************************************************************************

The CCNJCS^3 was formed in response to a growing interest among
members of the Psychology, Computer Science, and Neuroscience
departments at Stanford in learning about recent advances in the
study of computational approaches to modelling the relationship
between cognition and neuroscience. In addition to organizing
seminars, we also arrange journal club meetings in which graduate
students and post-docs meet to read and discuss current research
articles dealing with: The neural substrates of learning and memory,
computational models of neuronal processes, and the neural bases
of cognitive behavior.

For more information, contact Mark Gluck (gluck%su-psych@sumex-aim).

------------------------------

Date: Mon 14 Oct 85 07:52:23-PDT
From: Ana Haunga <[email-masked]>
Subject: Seminar - Probabilistic Interpretation of Certainty Factors (SU)

   SIGLUNCH will be held at the Chemistry Gazebo at 12:05-1:00 p.m.

     Probabilistic Interpretations for MYCIN's Certainty Factors

                           David Heckerman

I will show that the original definition of certainty factors (CF's)
is inconsistent with the "defining desiderata" of the CF combination
functions.  I will then show that if this inconsistency is removed by
redefining CF's in terms of the desiderata then CF's have
probabilistic interpretations.  In other words, I will show that
certainty factors are nothing more than transformed probabilistic
quantities.  The construction of the interpretation provides insights
into the assumptions made when propagating CF's through an inference
net.  For example, it can be shown that all evidence which bears
directly on a hypothesis must be conditionally independent on the
hypothesis and its negation.  After presenting the interpretations,
I will discuss several ramifications of the correspondence between
CF's and probabilities.

------------------------------

End of AIList Digest
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