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Neuron Digest Volume 06 Number 33
Neuron Digest Tuesday, 22 May 1990 Volume 6 : Issue 33
Today's Topics:
reply to reynolds
Item for Distribution
Neurobiological modeling paper
Preprint
reports available
Pragmatics in AI announcement
Send submissions, questions, address maintenance and requests for old issues to
"neuron-request@hplabs.hp.com" or "{any backbone,uunet}!hplabs!neuron-request"
Use "ftp" to get old issues from hplpm.hpl.hp.com (15.255.176.205).
------------------------------------------------------------
Subject: reply to reynolds
From: codelab@psych.purdue.edu (Coding Lab Wasserman)
Date: Mon, 21 May 90 09:40:19 -0500
MULTIPLE MEANING THEORIES
John Reynolds writes:
>"I'm very interested in "multiple meaning" theories (e.g. Raymond and
>Lettvin, and now Optican and Richmond), the informational role that
>conduction blocks in axon arbors might play, and the function of
>temporally modulated pulse codes in general.
>I'm writing in order to gather references to related work."
The foundation references for this problem include those which phrase it
most generally. It is not a pulse coding problem per se. Rather it is a
question of determining how information is encoded in any neural
response, whether it be a graded potential waveform or a train of spike
potentials. The classic contributions include:
Perkel, D.H. and Bullock, T.H. Neural coding. Neuroscience Research
Program Bulletin, 1968, 6, 221-347.
Uttal, W.R. The Psychobiology of Sensory Coding, 1973, Harper and
Row.
For a brilliant (i.e., Nobel-prize-winning) analysis of the problem
which reached exactly the wrong conclusion about this problem in
particular (while making major advances in other areas) and thereby
retarded progress in this area for 40 years, it is instructive to read:
Adrian, E.D. The basis of sensation. 1928, Christophers.
We have recently offered an analysis of the problem at the level of
receptor potential waveforms. A recent review is given in:
Nisly, S. and Wasserman, G.S.Intensity dependence of
perceived duration: Data, theories, and neural integration.
Psychological Bulletin, 1989, 106, 483-496.
The real problem, though, is in determining whether any plausible coding
scheme is actually employed by the nervous system (as opposed to the
investigator). For this, the best determination comes from a behavioral
assay.
Jerry Wasserman
Purdue University
------------------------------
Subject: Item for Distribution
From: B M Smith <bms%dcs.leeds.ac.uk@NSFnet-Relay.AC.UK>
Date: Thu, 10 May 90 11:44:31 +0100
CALL FOR PAPERS
AISB'91
8th SSAISB CONFERENCE ON ARTIFICIAL INTELLIGENCE
University of Leeds, UK
16-19 April, 1991
The Society for the Study of Artificial Intelligence and Simulation of
Behaviour (SSAISB) will hold its eighth biennial conference at
Bodington Hall, University of Leeds, from 16 to 19 April 1991. There
will be a Tutorial Programme on 16 April followed by the full Technical
Programme. The Programme Chair will be Luc Steels (AI Lab, Vrije Universiteit
Brussel).
Scope:
Papers are sought in all areas of Artificial Intelligence and Simulation of
Behaviour, but especially on the following AISB91 special themes:
* Emergent functionality in autonomous agents
* Neural networks and self-organisation
* Constraint logic programming
* Knowledge level expert systems research
Papers may describe theoretical or practical work but should make a
significant and original contribution to knowledge about the field of
Artificial Intelligence.
A prize of 500 pounds for the best paper has been offered by British
Telecom Computing (Advanced Technology Group). It is expected
that the proceedings will be published as a book.
Submission:
All submissions should be in hardcopy in letter quality print and
should be written in 12 point or pica typewriter face on A4 or 8.5" x
11" paper, and should be no longer than 10 sides, single-spaced.
Each paper should contain an abstract of not more than 200 words and a
list of up to four keywords or phrases describing the content of the
paper. Five copies should be submitted. Papers must be written in
English. Authors should give an electronic mail address where possible.
Submission of a paper implies that all authors have obtained
all necessary clearances from the institution and that an author will
attend the conference to present the paper if it is accepted. Papers
should describe work that will be unpublished on the date of the
conference.
Dates:
Deadline for Submission: 1 October 1990
Notification of Acceptance: 7 December 1990
Deadline for camera ready copy: 16 January 1991
Information:
Papers and all queries regarding the programme should be sent to
Judith Dennison. All other correspondence and queries regarding the
conference to the Local Organiser, Barbara Smith.
Ms. Judith Dennison Dr. Barbara Smith
Cognitive Sciences Division of AI
University of Sussex School of Computer Studies
Falmer University of Leeds
Brighton BN1 9QN Leeds LS2 9JT
UK UK
Tel: (+44) 273 678379 Tel: (+44) 532 334627
Email: judithd@cogs.sussex.ac.uk FAX: (+44) 532 335468
Email: aisb91@ai.leeds.ac.uk
------------------------------
Subject: Neurobiological modeling paper
From: Dan Kammen <kammen@aurel.cns.caltech.edu>
Date: Thu, 10 May 90 11:33:32 -0700
WE HAVE RECENTLY COMPLETED AND SUBMITTED (N. NETWORKS) THE FOLLOWING
PAPER WHICH SHOULD BE OF INTEREST BOTH TO PERSONS MODELING
NEUROBIOLOGICAL NETWORKS AND THOSE DESIGNING SELF-ORGANIZING
ALGORITHMS:
CORRELATIONS IN HIGH DIMENSIONAL OR ASYMMETRIC DATA SETS:
HEBBIAN NEURONAL PROCESSING
WILLIAM R.SOFTKY and DANIEL M. KAMMEN
Computation and Neural Systems Program
California Institute of Technology
Pasadena, CA 91125
ABSTRACT
The Hebbian neural learning algorithm that implements Principal Component
Analysis (PCA) can be extended for the analysis of more realistic forms
of neural data by including higher than 2-channel correlations and
non-Euclidean (l_P; l-sub-P) metrics. Maximizing a D-th rank tensor form
which correlates D channels is equivalent to raising the exponential
order of variance correlation from 2 to D in the algorithm that
implements PCA. Simulations suggest that a generalized version of Oja's
PCA neuron can detect such a D-th order principal component. Arguments
from biology and pattern-recognition suggest that neural data in general
is not symmetric about its mean; performing PCA with an implicit l_1
metric rather than the Euclidean metric weights exponentially distributed
vectors according to their probability, as does a highly nonlinear Hebb
rule. The correlation order D and the l_P metric exponent P were each
roughly constant for each of several Hebb rules simulated. We propose
and discuss a number of these generalized correlation algorithms in terms
of natural (biological) and artificial network implementations.
Keywords: Principal Component Analysis, Hebbian learning,
self-organization, correlation functions, multi-dimensional
analysis, non-Euclidean metrics, information theory, asymmetric coding.
Address correspondence or preprint requests to:
Dr. D. M. KAMMEN:
Division of Biology, 216-76
California Institute of Technology
Pasadena, CA 91125 USA
kammen@aurel.cns.caltech.edu
KAMMEN@CALTECH.BITNET
------------------------------
Subject: Preprint
From: Gregory Kohring <HKF218%DJUKFA11.BITNET@vma.CC.CMU.EDU>
Date: Fri, 11 May 90 10:02:26 +0600
The following preprint is currently available.
-- G.A. Kohring
Finite-State Neural Networks:
A Step Towards the Simulation of Very Large Systems
G.A. Kohring
HLRZ an der KFA Julich
(Supercomputing Center at the KFA Julich)
Abstract
Neural networks composed of neurons with Q_N states and synapses with
Q_Jstates are studied analytically and numerically. Analytically it is
shown that these finite-state networks are up to 25 times more efficient
at information storage than networks with continuous synapses. In order
to take the utmost advantage of networks with finite-state elements, a
multi-neuron and multi-synapse coding scheme is introduced which allows
the simulation of networks having over one billion couplings at a speed
of 7.1 billion coupling evaluations per second on a single processor of
the Cray-YMP. A local learning algorithm is also introduced which allows
for the efficient training of large networks with finite-state elements.
Key Words: Neural Networks, Multi-Spin Coding, Replica Method,
Finite-State Networks, Learning Algorithms
HLRZ-33/90
Send Correspondence and request for preprints to:
G.A. Kohring
HLRZ an der KFA Julich
Postfach 1913
D-5170 Julich, West Germany
e-mail: hkf218@djukfa11.bitnet
------------------------------
Subject: reports available
From: Gerhard Weiss <weissg@lan.informatik.tu-muenchen.dbp.de>
Date: 09 May 90 20:35:00 -0200
*** Do not use 'REPLY' ***
The following two reports are available.
COMBINING NEURAL AND EVOLUTIONARY LEARNING: ASPECTS AND APPROACHES
Report FKI-132-90
Gerhard Weiss
This report focusses on the intersection of neural and evolutionary
learning and shows basic aspects of and current approaches to the
combination of these two learning paradigms. Advantages and difficulties
of such a combination are described. Approaches from both the field of
artificial intelligence and the neurosciences are surveyed. A number of
related works as well as extensive references to further literature are
presented.
Contents: - Hybrid approaches in artificial intelligence
. Evolutionary design of artificial neural networks
. Evolutionary training of artificial neural networks
. Further hybrid approaches and related works
- Selective theories in the neurosciences
. The evolutionary selection circuits model of learning
(Conrad et.al.)
. The theories of selective stabilization of
synapses and pre-representations (Changeux et.al.)
. The theory of neuronal group selection (Edelman)
ARTIFICIAL NEURAL LEARNING
Report FKI-127-90
Gerhard Weiss
This report provides an introducing overview of the foundations and
the principles of learning in artificial neural networks.
Contents: - General aspects
(artificial neural nets / adaptation rules /
gradient-following / ...)
- Supervised learning
(perceptron convergence procedure / backprop / Boltzmann learning)
- Associative reinforcement learning
(associative reward-penalty algorithm / reinforcement comparison
procedures / REINFORCE algorithms)
- Unsupervised learning
(topology-preserving feature maps / adaptive resonance theory /
development of feature analyzing cells)
REQUESTS FOR COPIES: weissg@lan.informatik.tu-muenchen.dbp.de
-> Please use subject: FKI-127 or FKI-132 or FKI-127+132
-> Please leave only your physical address
-> Those who already asked for copies
will receive them without any further request
OTHER CORRESPONDENCE: weissg@tumult.informatik.tu-muenchen.de
or Gerhard Weiss
Institut fuer Informatik -H2-
Technische Universitaet Muenchen
Arcisstrasse 21
D-8000 Muenchen 2
Fed.Rep.Germany
------------------------------
Subject: Pragmatics in AI announcement
From: paul@NMSU.Edu
Date: Tue, 22 May 90 12:48:39 -0600
[[ Editor's Note: The final part of this announcement appears to be in
"troff" format for those of you who wish to create a hard copy to post on
near your local coffee urn. -PM ]]
PLEASE DISTRIBUTE THE FOLLOWING ANNOUNCEMENT IN YOUR DEPARTMENT/LABORATORY:
Cut---------------------------------------------------------------------------
PRAGMATICS IN ARTIFICIAL INTELLIGENCE
5th Rocky Mountain Conference on Artificial Intelligence (RMCAI-90)
Science Hall and Music Center Auditorium
New Mexico State University
Las Cruces, New Mexico, USA, June 28-30, 1990
PRAGMATICS PROBLEM:
The problem of pragmatics in AI is one of developing theories, models,
and implementations of systems that make effective use of contextual
information to solve problems in changing environments.
CONFERENCE GOAL:
This conference will provide a forum for researchers from all
subfields of AI to discuss the problem of pragmatics in AI.
The implications that each area has for the others in tackling
this problem are of particular interest.
COOPERATION:
American Association for Artificial Intelligence (AAAI)
IEEE Computer Society
SPONSORSHIP:
Association for Computing Machinery (ACM)
Computing Research Laboratory (CRL), NMSU
Special Interest Group on Artificial Intelligence (SIGART)
U S WEST Advanced Technologies and the Rocky Mountain Society
for Artificial Intelligence (RMSAI)
INVITED SPEAKERS:
The following researchers are invited to present papers
at the conference:
*Martin Casdagli, Los Alamos National Laboratory, Los Alamos USA
*Arthur Cater, University College Dublin, Ireland EC
*Jerry Feldman, University of California at Berkeley, Berkeley USA
& International Computer Science Institute, Berkeley USA
*Barbara Grosz, Harvard University, Cambridge USA
*James Martin, University of Colorado at Boulder, Boulder USA
*Derek Partridge, University of Exeter, United Kingdom EC
*Roger Schank, Northwestern University, Illinois, USA
*Philip Stenton, Hewlett Packard, United Kingdom EC
*Robert Wilensky, University of California at Berkeley Berkeley USA
SUBMITTED PAPERS:
In addition over 40 papers on pragmatics in AI have been accepted
for the conference.
THE LAND OF ENCHANTMENT:
Las Cruces, lies in THE LAND OF ENCHANTMENT (New Mexico),
USA and is situated in the Rio Grande Corridor with the scenic
Organ Mountains overlooking the city. The city is
close to Mexico, Carlsbad Caverns, and White Sands National Monument.
There are a number of Indian Reservations and Pueblos in the Land Of
Enchantment and the cultural and scenic cities of Taos and Santa Fe
lie to the north. New Mexico has an interesting mixture of Indian, Mexican
and Spanish culture. There is quite a variation of Mexican and New
Mexican food to be found here too.
GENERAL INFORMATION:
The Rocky Mountain Conference on Artificial Intelligence is a major
regional forum in the USA for scientific exchange and presentation
of AI research.
The conference emphasizes discussion and informal interaction
as well as presentations.
The conference encourages the presentation of completed research,
ongoing research, and preliminary investigations.
Researchers from both within and outside the region
are invited to participate.
DEADLINES:
Pre-registration: June 1st, 1990
Final papers due: June 1st, 1990
TRANSPORT:
Las Cruces, New Mexico is located one hour from El Paso, Texas on I-10
West. Participants can fly into El-Paso International Airport and
transport will be provided from and to the airport.
SOCIALS:
The conference will include a registration reception buffet,
going_away_party full-buffet, banquet and banquet speaker (+ $25.00),
and numerous refreshments,
HOTELS:
The Las Cruces Hilton has rooms for $47.00 per night.
(Call 1-800-284-0616, cutoff date is June 13th)
Accommodation is also available in other Hotels and Motels.
REGISTRATION:
Pre-Registration: Professionals: $50.00; Students $30.00
(Pre-Registration cutoff date is June 1st 1990)
Registration: Professionals: $70.00; Students $50.00
(at the conference)
(Copied proof of student status is required).
Registration form (IN BLOCK CAPITALS).
Enclose payment made out to New Mexico State University.
(ONLY checks in US dollars will be accepted).
Send to the following address (MARKED REGISTRATION):
Local Arrangements Chairperson, RMCAI-90
Computing Research Laboratory
Dept. 3CRL, Box 30001, NMSU
Las Cruces, NM 88003-0001, USA.
Name:_______________________________ E-mail_____________________________ Phone__________________________
Affiliation: ____________________________________________________
Fax: ____________________________________________________
Address: ____________________________________________________
____________________________________________________
____________________________________________________
COUNTRY__________________________________________
LOCAL ARRANGEMENTS:
Local Arrangements Chairperson, RMCAI-90.
(same postal address as above).
INQUIRIES:
Inquiries regarding conference brochure and registration form
should be addressed to the Local Arrangements Chairperson.
Inquiries regarding the conference program should be addressed
to the Program Chairperson.
Local Arrangements Chairperson: E-mail: INTERNET: rmcai@nmsu.edu
Phone: (+ 1 505)-646-5466
Fax: (+ 1 505)-646-6218.
Program Chairperson: E-mail: INTERNET: paul@sparta.nmsu.edu
Phone: (+ 1 505)-646-5109
Fax: (+ 1 505)-646-6218.
Paul Mc Kevitt,
Program Chairperson, RMCAI-90,
Computing Research Laboratory (CRL),
Dept. 3CRL, Box 30001,
New Mexico State University,
Las Cruces, NM 88003-0001, USA.
TOPICS OF INTEREST:
You are invited to submit a research paper addressing Pragmatics
in AI, with any of the following orientations:
Philosophy, Foundations and Methodology
Knowledge Representation
Neural Networks and Connectionism
Genetic Algorithms, Emergent Computation, Nonlinear Systems
Natural Language and Speech Understanding
Problem Solving, Planning, Reasoning
Machine Learning
Vision and Robotics
Applications
TENTATIVE CONFERENCE SCHEDULE:
.ce
\fBRMCAI-90 CONFERENCE SCHEDULE\fR
WEDNESDAY 27th June 1990:
6:00 pm - 10:00 pm: Registration and Reception, Double Eagle, Old Mesilla
THURSDAY 28th June 1990:
\fB8:50 am: Yorick Wilks and Paul Mc Kevitt: Welcome\fR
\fB9:00 am: Invited talk: Jerry Feldman, UC Berkeley \fR
.nf
.ta .6i
Miniature Language Acquisition: A Paradigm problem and some approaches
10:00 am: Coffee
10:30 am - 12:30 pm: Three tracks of submitted papers.
.nf
\fBTRACK A:\fR
PRACMA: Processing Arguments between Controversially-Minded Agents
Jurgen Allgayer : Alfred Kobsa : Carola Reddig : Norbert Reithinger
Relevant Beliefs
Afzal Ballim : Yorick Wilks
Speech Acts and Mental States
Robbert-Jan Beun
Extensions of Constraints on Speech Act Ambiguity
Elizabeth A. Hinkelman
\fBTRACK B:\fR
Dynamic Route Planning
E. Cortes-Rello : F. Golshani
Strategic Planning System (SPS)
Mitchell Smith : Peter Briggs : Edward Freeman
Re-planning a Route - A Pragmatic Approach
Wai-Kiang Yeap
Evaluation of Pragmatics Processing in a Direction Finding Domain
Deborah A. Dahl
\fBTRACK C:\fR
Computing with Fast Modulation: Experiments with Biologically
Realistic Model Neurons
Mark DeYong : Randall Findley : Chris Fields
Competition and Selection in Neural Networks with Distributed
Representations
Kankanahalli Srinivas : John Barnden
Using Genetic Algorithms as a Post-Processor for Improving Vehicle
Routing Solutions
Nagesh Kadaba : Kendall E. Nygard
An Application of Neural Networks is Robotics
Dr. Behzad Ghavimi
12:30 pm - 2:00 pm: Lunch
\fB2:00 pm: Invited talk: Robert Wilensky, UC Berkeley, USA\fP
3:00 pm - 3:30 pm: Coffee
\fB3:30 pm - 4:30 pm: Invited talk: Phil Stenton, HP Laboratories, Bristol, UK\fP
.nf
.ta 1.2i
Putting NL to work: A dialogue modeling approach
.sp
.fi
4:30 pm - 5:30 pm: Three tracks of submitted talks
\fBTRACK A:\fR
.sp
.nf
.ta .6i
Using relational knowledge structures to handle null value situations
in natural language interfaces
Nick Cercone : Dan Fass : Chris Groeneboer : Gary Hall : Mimi Kao :
Paul McFetridge : Fred Popowich
A Classification of User-System Interactions in Natural Language
with Special Reference to :
Dan Fass : Nick Cercone : Gary Hall : Chris Groeneboer :
Paul McFetridge : Fred Popowick
\fBTRACK B:\fR
Problem Solving Experience and Problem Solving Knowledge
Stephen W. Smoliar
An Abstraction-Partitioned Model for Reactive Planning
Lee Spector : James A. Hendler
\fBTRACK C:\fR
A Graph Theoretic Basis for Problem Solving
Daniel P. Eshner : Heather D. Pfeiffer
Meta-Structures: Intelligent Structures for Inference Control
Daniel J. Goter : David E. Monarchi
FRIDAY 29th June 1990:
\fB9:00 am: Invited talk: Barbara Grosz, Harvard University\fP
Collaborative Planning for Discourse
10:00 am: Coffee
10:30 am - 12:30 pm: Three tracks of submitted papers
\fBTRACK A:\fR
Why Does Language Matter to Artificial Intelligence
Marcelo Dascal
Pragmatics of Postdeterminers Non-restrictive Modifications & Wh-phrases
Frens J.H. Dols
Pragmatics and Natural Language Processing
Eduard H. Hovy
On the Semantics of the Conjunction "but"
Wlodek Zadrozny : Karen Jensen
\fBTRACK B:\fR
How to Become Immune to Facts
M.J. Coombs : R.T. Hartley : W.B. Kilgore : H.D. Pfeiffer
Constrained Rational Agency
Bruce D'Ambrosio : Tony Fountain : Lothar Kaul
Abductive Inference in AI: Potential Unifications
Venugopala Rao Dasigi
A Prolog Implementation of the Stable Model TMS
Stephen Pimentel : John L. Cuadrado
\fBTRACK C:\fR
Multiple Level Island Search
Peter C. Nelson : John F. Dillenburg
Efficient Learning with Representative Presentations
Xiaofeng (Charles) Ling
User Modelling in a Knowledge-Based Environment for European Learning
Michael F. McTear : Norman Creaney : Weiru Liu
Training a Neural Network to be a Context Sensitive Grammer
Robert F. Simmons : Yeong-Ho Yu
12:30 pm - 2:00 pm: Lunch
\fB2:00 pm: Invited talk: Roger Schank, Northwestern University\fP
3:00 pm - 3:30 pm: Coffee
\fB3:30 pm - 4:30 pm: Invited talk: Arthur Cater, University College Dublin, Ireland\fP
4:30 pm - 5:30 pm: Three tracks of submitted papers
\fBTRACK A:\fR
Towards Empirically Derived Semantic Classes
Brian M. Slator : Shahrzad Amirsoleymani : Sandra Andersen : Kent Braaten
John Davis : Rhonda Ficek : Hossein Hakimzadeh : Lester McCann :
Joseph Rajkumar : Sam Thangiah : Daniel Thureen
Using Words
Louise Guthrie : Paul Mc Kevitt : Yorick Wilks
\fBTRACK B:\fR
An Expert Tool for Digital Circuit Design
F.N. Sibai : K. L. Watson
Explaining Control Strategy in Second Generation Expert Systems
Xuejun Tong
\fBTRACK C:\fR
A New Approach to Analyzing Aerial Photographics
Dwayne Phillips
Acquiring Categorical Aspects: A Connectionist Account of Figurative
Noun Semantics
Susan Hollbach Weber
6:00 pm - 9:00 pm: Japanese Buffet in Garden Center (Budagher's)
SATURDAY 30th June 1990:
\fB9:00 am: Invited talk: Derek Partridge, University of Exeter, UK\fP
10:00 am: Coffee
10:30 am - 11:30: Two tracks of submitted papers
\fBTRACK A\fR
An Experiment on Technical Text Reproduction
Wanying Jin
Explanation Dialogues: Interpreting Real Life Questions & Explanations
Efstratios Sarantinos : Peter Johnson
Modeling of mind and its application to image sequence understanding
Naoyuki Okada
\fBTRACK B:\fR
Communication and Belief Changes in a Society of Agents
Graca Gaspar
An Interval Calculus Based Finite Domain Constraint and
its Implementation in Prolog
Jin-Kao Hao : Jean-Jacques Chabrier
Dynamic Context Diagrams:
the pragmatics of social interaction in KBS development
Simon P.H. Morgan
11:30 am - 1:30 pm: Lunch
\fB1:30 pm - 2:30 pm: Invited talk: James Martin, University of Colorado at Boulder\fP
.nf
.ta 1.2i
A Unified Approach To Conventional Non-Literal Language
3:00 pm - 3:30 pm: Coffee
\fB2:30 pm - 3:30 pm: Invited talk: Martin Casdagli, Los Alamos National Laboratories\fP
Pragmatic Artificial Neural Nets for the Nonlinear Prediction of Time Series
6:00 pm - 9:00 pm: Banquet (Double Eagle)
.ce
*****************************
PROGRAM COMMITTEE:
*John Barnden, New Mexico State University
(Connectionism, Beliefs, Metaphor processing)
*Hans Brunner, U S WEST Advanced Technologies
(Natural language interfaces, Dialogue interfaces)
*Martin Casdagli, Los Alamos National Laboratory
(Dynamical systems, Artificial neural networks, Applications)
*Mike Coombs, New Mexico State University
(Problem solving, Adaptive systems, Planning)
*Dan Eshner, University of Maryland
(Planning, Search, Knowledge Representation)
*Thomas Eskridge, Lockheed Missile and Space Co.
(Analogy, Problem solving)
*Chris Fields, New Mexico State University
(Neural networks, Nonlinear systems, Applications)
*Roger Hartley, New Mexico State University
(Knowledge Representation, Planning, Problem Solving)
*Victor Johnson, New Mexico State University
(Genetic Algorithms)
*Paul Mc Kevitt, New Mexico State University
(Natural language interfaces, Dialogue modeling)
*Joe Pfeiffer, New Mexico State University
(Computer Vision, Parallel architectures)
*Keith Phillips, University of Colorado at Colorado Springs
(Computer vision, Mathematical modelling)
*Roger Schvaneveldt, New Mexico State University
(Knowledge representation, Knowledge elicitation, cognitive modeling)
*Brian Slator, North Dakota State University
(Natural language processing, Knowledge acquisition)
*Yorick Wilks, New Mexico State University
(Natural language processing, Knowledge representation)
*Scott Wolff, U S WEST Advanced Technologies
(Intelligent tutoring, User interface design, Cognitive modeling)
Organizing Committee RMCAI-90:
Paul Mc Kevitt Yorick Wilks
Research Scientist Director, CRL
CRL and Professor, NMSU Computer Science
cut------------------------------------------------------------------------
------------------------------
End of Neuron Digest [Volume 6 Issue 33]
****************************************
