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Neuron Digest Volume 06 Number 61
Neuron Digest Thursday, 18 Oct 1990 Volume 6 : Issue 61
Today's Topics:
A neural net who plays chess
Re: A neural net who plays chess
Re: A neural net who plays chess
Re: A neural net who plays chess
Re: A neural net who plays chess
Re: A neural net who plays chess
NN for weather forecasting
Neural Net Visual Servoing Systems
posting for Neuron Digest
Weather forecasting References
Re: Weather forecasting References
Kohonen's Self Organizing Map for Pattern Recognition
neural nets applications to controls
REFERENCES NEEDED
Seismic analysis
Neuro-Nimes'90
COGNITIVE SCIENCE/HCI INITIATIVE
Talk at American University
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: A neural net who plays chess
From: swrinde!cs.utexas.edu!sdd.hp.com!zaphod.mps.ohio-state.edu!uwm.edu!csd4.csd.uwm.edu!markh@ucsd.edu (Mark William Hopkins)
Organization: University of Wisconsin-Milwaukee
Date: 04 Oct 90 07:17:59 +0000
I've been sitting on this extremely simple but potentially very
powerful design which would enable a neural net to learn to play chess
effectively, starting even from scratch. Haven't gotten around to coding
it yet, or testing it, but if you'd like to try it yourself, I've posted
a specification below.
(1) ARCHITECTURE
The design is actually of a hybrid architecture: embodying both your
typical game-playing/search AI program and a typical neural net. Simply
put, the search program (the "left hemisphere") acts as a 'predictor'.
It searches out the best configurations N moves ahead of the current
configuration. The neural net (the "right hemisphere") acts as an
'evaluator'. It's purpose is to fill in the role formerly held by the
hard-coded heuristics you usually would have otherwise found in a
chess-playing program. These two components reenforce each other in such
a way that the evaluator gradually learns to "squash" more and more of
the N-move search tree into a static global evaluation function. This,
of course, serves to increase the effectiveness of the predictor.
(a) Evaluator
The input to the evaluator is a board configuration which contains
information to allow you to completely determine the position of any
piece on the board (or whether it has been captured or not). It can be
as simple as a matrix, or as complex as a net with 'diagonal detectors',
'knight-L detectors', etc. built directly into the input representation.
Alternatively, it might just be a list of board locations indexed by
piece. The output is a number, say, between -1 and 1, indicating the
value of the board. A 1 indicates that the board is a winning board from
the program's point of view, and -1 ... a losing configuration.
(b) Predictor
The predictor is a program to implement search, such as minimax with a
cutoff. At the cutoff, the evaluator is used to estimate the
configuration in question. This allows the predictor to return with a
value in short finite time.
It also contains the body of rules that determine what comprises a
valid move, and a winning, losing, or stalemate configuration.
(2) INITIALIZATION
The evaluator MAY be initialized by training it using an already available
evaluation function as a supervisor to incorporate already developed
expertise (why re-invent the wheel?), or can simply be set to a random
configuration to simulate the process of learning the game from scratch.
(3) THE PLAY CYCLE
(a) Move generation
As mentioned above, to generate a move, the predictor performs a search
and uses the evaluator at the cut-off point. It comes back with a best
possible move AND an estimate as to the value of the current
configuration.
(b) Learning
Upon completion of a move, the evaluator is updated with the training pair
(value, configuration).
After the opponent makes a move, the play cycle is then resumed at (1).
Play continues, of course, until the program or its opponent wins (or,
using standard rules, until a draw or stalemate happens).
(4) EXPECTED PERFORMANCE
Provided the network was designed with care, I expect to see impressive
gains in playing ability over time.
(a) Static evaluation and search-tree squashing. As time goes on,
because of the feedback the predictor is providing to the
evaluator, the evaluator will actually learn to make static
evaluations of the current board, effectively squashing the N-move
search tree into a single global evaluation. This kind of static
evaluation in lieu of searching is precisely what good chess
players claim to be able to do.
(b) Spontaneous emergence of openings. After getting its butt
severely kicked the first few hundred thousand games (again, we're
talking about the case where the evaluator starts from
zero-knowledge), natural pathways will develop that correspond to
the openings typically taught to a novice.
(c) Spontaneous emergence of endgames. If the input network
architecture has been designed properly, the evaluator may be able
enough to make generalizations (say, of tranalation-invariance) to
embody knowledge of the more-or-less standard endgame
configurations.
(5) EMBEDDING THE PREDICTOR
An interesting extension to the design specified above would be to embed
the search control structure of the predictor itself in a neural net. If
this is done, then the hybrid "crutch" (that's all it really is: a
crutch) can finally be discarded.
------------------------------
Subject: Re: A neural net who plays chess
From: dave@cogsci.indiana.edu (David Chalmers)
Organization: Indiana University, Bloomington
Date: 06 Oct 90 08:20:21 +0000
>the search program (the "left hemisphere") acts as a 'predictor'.
>[[...]]
There is the seed of a very nice idea here. i.e., the best evaluation
function in chess is a fixed point of the "minimax search" operator in
function space. (Spelled out, that means that for an optimal evaluation
function E from positions to values, evaluating the position via minimax
search, applying E at the end of the search tree, should provide exactly
the same result as applying E directly to the position.) Combined with
certain constraints on E, e.g. E(checkmate position) = 1, E(checkmated
position) = -1, E(2 kings) = 0, this characterization should provide a
very tightly constrained evaluation function that would play a mean game
of chess. So, is there any way to take advantage of this by using
methods of fixed-point analysis? I confess to know almost nothing about
computer chess, so I don't know if the idea is commonplace there.
Your suggestion of bootstrapping the evaluator with the predictor is a
nice way of taking advantage of this property. There are a couple of
problems, though...
> (b) Learning
> Upon completion of a move, the evaluator is updated with the training pair
>(value, configuration).
>(4) EXPECTED PERFORMANCE
> Provided the network was designed with care, I expect to see impressive
>gains in playing ability over time.
> (a) Static evaluation and search-tree squashing.
> (b) Spontaneous emergence of openings.
> (c) Spontaneous emergence of endgames.
You may be overestimating the capabilities of networks just a tad.
Network training isn't magic... networks are ultimately pretty simple
structures, not great for computing incredibly complex mappings like the
ones required here (yes, I know that you can get universal approximation,
but only at the cost of generalization). After all, if you expect the
above to work, why not just try: train the network directly on (position,
move) pairs directly derived from the games of Kasparov and other
grandmasters. We'll get a grandmaster network, easy! And once we've
done that, why not try: train a network to simulate the total performance
of a person in all domains by training it from data provided over the
lifetime of a person: (sensory input at time t, motor output at time t).
Human intelligence, just like that! Of course, you'll need to use a
recurrent network, but that's no problem, we can just train it up with
back-propagation through time or the Williams/Zipser algorithm. I think
you see the problem by now...
>(2) INITIALIZATION The evaluator MAY be initialized by training it using
>an already available evaluation function as a supervisor to incorporate
>already developed expertise (why re-invent the wheel?), or can simply be
>set to a random configuration to simulate the process of learning the
>game from scratch.
One thing you don't want to do is to start completely from scratch. Even
bootstrapping needs to start from somewhere. All this would guarantee is
that you would arrive at *some* fixed point of the minimax operator --
but there are a lot of these -- the constant zero function for instance.
You have to make your initial evaluator minimally competent
(incorporating at minimum the constraints mentioned in the first
paragraph above, and preferably more) if you want bootstrapping to get
anywhere.
Actually, as things stand there's no need for the network to play actual
games at all. You just generate positions randomly, calculate the
minimax+evaluator prediction, and train the evaluator on that. (Only one
step of minimax search is required, nothing deep.) In fact, your
proposal doesn't incorporate any feedback from wins and losses, so
playing games doesn't serve any purpose at all (except perhaps to confine
the training of evaluation to a "reasonable" portion of position space).
And by turning it into a fully-supervised learning problem, you've made
it much easier than it would be with the kind of reinforcement learning
that would be required from playing actual games, with all the
accompanying credit-assignment problems.
Nice idea, and I'm not sure that it couldn't be taken advantage of in
various interesting ways. Nothing about this need be specific to neural
networks, incidentally -- any kind of learning system would do. Just one
that has an architecture that guarantees both complete learning and
perfect generalization for the problem at hand. Which sadly doesn't
exist in 1990, but hey, that's only a minor point...
Dave Chalmers (dave@cogsci.indiana.edu)
Concepts and Cognition, Indiana University.
"It is not the least charm of a theory that it is refutable."
------------------------------
Subject: Re: A neural net who plays chess
From: markh@csd4.csd.uwm.edu (Mark William Hopkins)
Organization: University of Wisconsin-Milwaukee
Date: 08 Oct 90 23:58:34 +0000
In article <62519@iuvax.cs.indiana.edu> (David Chalmers) writes:
>... In fact, your proposal doesn't incorporate any feedback from wins
>and losses, so playing games doesn't serve any purpose at all (except
>perhaps to confine the training of evaluation to a "reasonable" portion
>of position space)...
It wasn't made explicit, but the search program (where the rules and
winning, losing and draw conditions are stored) provides the feedback to
the evaluator.
------------------------------
Subject: Re: A neural net who plays chess
From: mahler@latcs1.oz.au (Daniel Mahler)
Organization: Comp Sci, La Trobe Uni, Australia
Date: 09 Oct 90 13:22:08 +0000
I did my honours thesis on using NN's learn to play games. I used a
program based on the principle you describe to learn noughts and crosses.
There are 2 points worth making about this aproach.
1) It is essentially an extension of the learning technique in Samuel's
checkers program. Samuel used an evaluation function that was a linear
combination of given feature functions. These weights were optimised by
getting the minimax value for a position using the current weights, and
using it as a training signal. Samuel worked before the perceptron era,
so he did not call it a perceptron. His technique worked because the
feature selectors were designed by a human expert. I tried the natural
extension: multilevel perceptron with bp, working from a simple
representation of the board and trying to see if the net can develop
tactical and strategic concepts. I started with random weights, because
my interests were mainly theoretical (that's what I always say when
something doesn't work 8>) ) Part of the reason for my techniques failure
is the second point.
2) The minimax operator does not have a unique fix-point (I like this
terminology, shame it's to late for my thesis). In fact it has
infinitely many, as any constant value function is a fix-point. This
means, in NN terms, lots and lots of local minima. Supplying known
strategic features as inputs simplifies the energy space. This kind of
learning is probably affected by game tree pathology, especially in the
early stages.
I think the solution to this problem is to incorporate a genetic
techniques with large populations and/or use reinforcement learning
(Barto & Sutton's Arp element). Samuel (and I) used a sort of genetic
technique with population size 2.
There are 2 nets that play each other called champion and challenger, or
master and apprentice. The weights in champion are fixed. Challenger
learns by the method under discussion. When challenger outperforms
champion by some criterion, it becomes champion and a new random
challenger is generated.
This leads to peculiarities which i ascribe to 'inbreeding'. The graph
of the number of games taken by each successive challenger to become
champion is a saw-tooth. Each new challenger takes longer than the last,
(which I interpret as each champion being better) until suddenly there is
a champion that lasts a very short time, and then the cycle repeats,
though the peaks seem to get higher. (fractals fanatics may wonder if
there are peaks within peaks). I take this to mean that eventually there
evolve nets designed to beat the champion though they can be beaten by a
random player.
Daniel Mahler
beating
D
A
------------------------------
Subject: Re: A neural net who plays chess
From: mahler@latcs1.oz.au (Daniel Mahler)
Organization: Comp Sci, La Trobe Uni, Australia
Date: 10 Oct 90 08:46:46 +0000
Several people have pointed out that my claim about trivial
fixpoints is not true as your program should recognise terminal game
positions and give absolute value to these. You of course have to do
this to give bp some goal directed bias, but for a game of any complexity
there will be only few terminal nodes in your search horizon. There will
usually be some in chess but NONE in go or othello until the endgame.
For noughts and crosses we can overcome all the problems by
tabulating all the legal positions and their true min-max values. This
is feasible as there are less than 3^9 legal positions to evaluate. It
happens in a few seconds of real-time. Then you do not have to worry
about using genetics to avoid inbred strategies, but you do not have to
worry about neural nets or learning either.
I gave my tic-tac-toe program a limited horizon because I was
intersted in going onto go (bravery is usually born of
innocence/ignorance).
Overall, I do not think this generalised Samuel's technique is
viable for learning from scratch, which is what machine learning people
really want. :) (For this Lenat's Eurisco, genetics or Barto&Sutton seem
more promising) First it is computationally very expensive: to produce
the teaching signal you must do a search and eavaluate a neural net at
each terminal node. Initially this signal will be poor anyway. It is
likely that tree pathology (Nau's work) becomes a significant factor
under these circumstances. Also for a complex game the net must learn to
cluster positions by tactical/stragic concepts from the extremely small
percentage of all legal positions it will ever see.
However, a slight modification of Samuel's idea seems good. Use
feature detectors designed by experts as inputs to a small net. This
will then be Samuels method extended to allow nonlinear combinations of
these features as evaluation functions.
Daniel
------------------------------
Subject: Re: A neural net who plays chess
From: markh@csd4.csd.uwm.edu (Mark William Hopkins)
Organization: University of Wisconsin-Milwaukee
Date: 11 Oct 90 06:50:22 +0000
In article <8966@latcs1.oz.au> mahler@latcs1.oz.au (Daniel Mahler) writes:
On the emergence of goal-directed behavior:
> Several people have pointed out that my claim about trivial
>fixpoints is not true as your program should recognise terminal game
>positions and give absolute value to these. You of course have to do
>this to give bp some goal directed bias, but for a game of any
>complexity there will be only few terminal nodes in your search horizon.
>There will usually be some in chess but NONE in go or othello until the
>endgame.
The evaluator is always learning based on inputs from positions N moves
ahead. Initially it will only have substantial input within N moves of
the end, but this learning propagates as it takes THIS input in later
games and evaluates N moves from *it*: now 2N moves from the end. And so
on... The evaluator's trying to converge onto a non-trivial fixed point.
The goal directed behavior should propagate from bottom up, as it were.
If it really learns, the first thing you'll notice is that it'll
progressively avoid bad openings one by one because they lead to quick
endings. Eventually, by exclusion, you'll have the good openings left.
In that sense, the bottom-up learning also leads to top-down learning.
I think the relevant issues are probably whether the network will be big
enough to handle the knowledge a typical chess master has of the state
space, and whether it can learn quick enough without having to beating it
against a wall a hundred thousand times just to even get it to do
something as easy as picking up a simple boolean function?
On using hard-coded feature detectors to expedite learning:
> However, a slight modification of Samuel's idea seems good. Use
>feature detectors designed by experts as inputs to a small net. This
>will then be Samuels method extended to allow nonlinear combinations of
>these features as evaluation functions.
What's to keep the evaluator neural net from spontaneously generating
nodes that emulate feature detection in its hidden layer(s) anyhow?
As a concrete example on a simpler game try this design on tic-tac-toe:
LAYER 3: 1 node (output).
LAYER 2: N nodes (try N = 12).
LAYER 1: 9 nodes (input .. connected to tic-tac-toe grid)
For input, a square occupied by the program's piece evaluates to +1, a
square occupied by an opponent's piece to -1, and an empty square to 0.
Every cell is connected to every other cell in an adjacent layer. Use
back-propagation *with thresholds*. Take your learning input from a
minimax search program that looks merely 1 move ahead using the output
node of the evaluator function to resolve all
non-winning/non-losing/non-draw positions.
When using +1, and -1 as limmiting activation values the sigmoid function
1/(1 + e^(-X)) becomes tanh(X), by the way, and the error correction
factor Y(1 - Y) becomes 1 - Y*Y.
I predict two things will happen:
(1) The program, when playing X, will open in the middle square
with increasing frequency. When playing O, it will move into a
corner in response to an X placed in the middle more and more
frequently.
(2) Some of the hidden-layer nodes will evolve spontaneously into
3-in-a-row detectors.
------------------------------
Subject: NN for weather forecasting
From: muttiah@stable.ecn.purdue.edu (Ranjan S Muttiah)
Organization: Purdue University Engineering Computer Network
Date: 05 Oct 90 02:35:54 +0000
I am looking for any references that deal with this topic. Has anyone
looked at time series using neural net ? email me if you would please.
------------------------------
Subject: Neural Net Visual Servoing Systems
From: Keith Nicewarner <nicewarn@ral.rpi.edu>
Date: Thu, 11 Oct 90 21:36:57 -0400
I am currently developing a frame-rate visual servoing system for a PUMA
robot arm. I am using a camera mounted on the gripper of the robot for
feedback. The objective is to use this information to guide the gripper
towards an object in the field of view, matching the position and
orientation of the gripper with the object.
My current options are to use localized Jacobian techniques or
traditional inverse-kinematics, assuming that the position and
orientation of the object (specifically, a cyllinder of known diameter)
can be derived using special markers on the object.
I have looked at a few neural network systems which deal with the problem
of visual servoing, specifically, the various hand-eye coordination
systems developed by Grossberg and Bullock. These models, however, were
developed with only the intent of simulating and understanding biological
systems. My needs require a practical, fast, and easy to impliment
system which doesn't necessarily have to be all that accurate.
Does anyone have any information about neural network visual servoing
systems, or more generally, hand-eye coordination or object tracking
systems? Feedback will be greatly appreciated.
Keith Nicewarner
Center for Intelligent Robotic Systems for Space Exploration
Renssalaer Polytechnic Institute
------------------------------
Subject: posting for Neuron Digest
From: daft@debussy.crd.ge.com (Chris Daft)
Date: Fri, 12 Oct 90 21:17:41 -0400
A while ago I asked net-people for their ideas on what the best work was
on neural nets and image processing/image analysis. I got lots of
helpful mail and have now written a review article on it. Some folks
asked if they could get a copy of this: if you want one, send me a
message. Unfortunately, it does not exist as a file you can ftp, but I
will be happy to snail-mail it to you. The paper will be published in
the proceedings of the 1990 IEEE Ultrasonics Symposium.
Chris Daft, GE Corporate R&D Center.
------------------------------
Subject: Weather forecasting References
From: muttiah@stable.ecn.purdue.edu (Ranjan S Muttiah)
Organization: Purdue University Engineering Computer Network
Date: 13 Oct 90 04:21:32 +0000
I wish to thank those who responded to my request.
There was one paper by Widrow and Hoff for weather forecasting (using
madaline ?) in the early 60's that I'm trying to locate. Anyone know of
this paper ?
-------------------------
For predicting time series:
Moody, J. Darken, C., 1989, Fast Learning in Networks of Locally-Tuned
Processing Units, Neural Computation, 1(2), pp. 281-294
For predicting weather:
Rogers, D., 1990, Predicting Weather Using a Genetic Memory: A
Combination of Kanerva's Sparse Distributed Memory with Holland's Genetic
Algorithms, Advances in Neural Information Processing Systems, vol 2, pp.
455-464
One paper I have found particularly interesting is
Predicting the future: A Connectionist Approach A.S Weigend, B.A Huberman
and D.E Rumelhart, 1990 Stanford Universtity technical report
Standford-PDP-90-01 Submitted to the International Journal of Neural
Systems.
in which the authors describe their work in perdicting future values of
chaotic time series using backprop networks. It is based on a previous
paper:
Nonlinear Signal processing using neural networks: prediction and
system modelling.
A.S Lapedes and R.M Farber
Technical report LA-UR-87-2662
Los Alamos National Laboratory, 1987
------------------------------
Subject: Re: Weather forecasting References
From: aboulang@bbn.com (Albert Boulanger)
Organization: BBN, Cambridge MA
Date: 13 Oct 90 20:01:29 +0000
One other excellent paper that represents this approach (chaotic time
series prediction) is:
"Nonlinear Forcasting as a Way of Distinguishing Chaos from Measurement
Error in Time Series"
George Sugihara & Robert M. May
Nature, Vol344, 19 April 1990, 734-741
They give examples where the nonlinear perdiction technique works AND
(contrary to what one sees in the AI world) does not work.
Regards,
Albert Boulanger
aboulanger@bbn.com
------------------------------
Subject: Kohonen's Self Organizing Map for Pattern Recognition
From: "M.Elif KARSLIGIL" <ELIF%TRYILDIZ.BITNET@CUNYVM.CUNY.EDU>
Date: Fri, 12 Oct 90 17:35:09 -1100
I'm looking for some information about Kohonen's Self Organizing Map for
Pattern Recognition. Any references will be much appreciated..
Elif Karsligil
Yildiz University
Computer Sciences and Engineering Dept.
E-Mail:Elif at Tryildiz
Address: YILDIZ UNIVERSITESI
EHBAM 80150
Yildiz-ISTANBUL
TURKEY
------------------------------
Subject: neural nets applications to controls
From: ELEE6UG@jetson.uh.edu
Date: Sat, 13 Oct 90 21:48:00 -0500
Hi
I have just been introduced to the neuron digest . I am a graduate
student at the University of houston.My current interest is neural
networks applications to linear and nonlinear controls.
I was working in nonlinear dynamic controls when I came accross a paper
by CHARLES W ANDERSON -"Neuronlike Adaptive Elements that can Solve
difficult Learning Control Problems." IEEE Transactions on systems Man
and Cybernetics-vol-smc-13,no 5,september/ october 1983
The paper described a neuronlike adaptive element system (search and
critic) to control a clasical control problem, stabilizing a cart mounted
inverted pendulum system.The very idea to control a system(nonlinear in
this case)without knowing the dynamics of the system got me interested in
NN.
However all the other papers (that I have read) ,including the one
mentioned, that followed to solve a similar problem, never showed results
when the system was REALLY nonlinear.for example ,for those familiar with
the above mentioned problem ,results were not available when the starting
angle of the inverted pendulum is large (more than 54 degrees).For a
small initial angle the system can be approximated to a linear system.
I am interested in hearing from people that share a comnmon interest. I
have been introduced to NN only a few months ago .I would appreciate if
some of you could suggest a good reading on NN and controls.
Sidharth Sibal
Email:ELEE6ug@uhvax1.uh.edu
------------------------------
Subject: REFERENCES NEEDED
From: qian@icopen.ICO.OLIVETTI.COM (DA QUN QIAN)
Date: Sun, 14 Oct 90 09:53:55 +0100
I am looking for references on automatic modifcation of structure of a
neural network by learning algorithms(i.e. automatically add some links
and nodes to a neural network or remove them from the neural network.
Any information are welcomed.
Thanks in advance.
Qian Da Qun
Artificial Inteligeence Center
Olivetti Nuova ICO 3 Piano
Via Jervis 77, 10015 Ivera(TO)
ITALY
Eamil: qian@icopen.ico.olivetticom
------------------------------
Subject: Seismic analysis
From: quark@xroads.UUCP (Jerry Rightnour)
Organization: Crossroads, Phoenix, AZ 85046
Date: 14 Oct 90 23:12:38 +0000
I saw the following presentation announcement in the NIPS 1990 schedule
in the Neuron Digest V6 #58.
>App7* "Seismic Event Identification Using Artificial Neural Networks",
>by John L. Perry and Douglas Baumgardt.
I know some geologists who might be interested in this work.
Could someone provide an address (email or USmail) or phone number
for contacting the authors?
Also, any other references to work on computer analysis of seismic
signals would be appreciated. (Not limited to neural net approaches.)
Jerry Rightnour work 602-862-5918
14020 N. Black Canyon #2024 home 602-375-8409
Phoenix,AZ 85023 email quark@xroads.uucp
\ / C r o s s r o a d s C o m m u n i c a t i o n s
/\ (602) 941-2005 300|1200|2400 Baud 24 hrs/day
/ \ hplabs!hp-sdd!crash!xroads!quark
------------------------------
Subject: Neuro-Nimes'90
From: Francisco Castillo Cobo <castillo@eel.upc.es>
Date: 18 Oct 90 13:28:00 +0100
I would like to take this opportunity to invite everyone in the field of
Neural Networks to the upcoming Neuro-Nimes '90 conference to be held
from Nov.12-16,1990 in Nimes, France. It also includes, apart from the
conference, some tutorials and an exhibi t ion. For more information
please contact:
Marie-Martine Sainflou
EC2
269-287, rue de la Garenne
92024 Nanterre Cedex - France
Tel:+331.47.80.70.00 Telex 612 469
Telefax: +331.47.80.66.29
Please do not respond to this message!
------------------------------
Subject: COGNITIVE SCIENCE/HCI INITIATIVE
From: Dr L S Smith (Staff) <lss@compsci.stirling.ac.uk>
Date: Wed, 17 Oct 90 14:49:58 +0100
COGNITIVE SCIENCE/HCI INITIATIVE
Department of Psychology
University of St Andrews
Scotland, UK
and
Centre for Cognitive and Computational Neuroscience
University of Stirling
Scotland, UK
2 POST-DOCTORAL RESEARCH FELLOWSHIPS
investigating psychological and neurocomputational
processes of visual word recognition.
This project represents a major collaboration between the Department of
Psychology, St Andrews (a leading centre for human perceptual research)
and the CCCN, Stirling (a leading centre for neural network research) to
develop a new computational model of visual word recognition.
Applications are invited for the following 2 post-doctoral fellowships
within the project:
Post 1 (tenured for 3 years, based at Department of Psychology,
St Andrews University) will involve developing fresh perspectives on the
neural modelling of visual word recognition from human experimentation.
The data from these experiments will form the basis for the
computational modelling in the project. Applicants should have
experience in human experimentation in cognitive science or perceptual
research, be well acquainted with the use of computers in
experimentation, and have some knowledge of neural network research.
Post 2 (tenured for 2 years, based at Centre for Cognitive and
Computational Neuroscience, Stirling University) will involve setting up
and developing a new computational model of visual word recognition
which combines the findings from St Andrews with fresh perspectives on
neurocomputational processing. Applicants should have experience or
interest in neural computation/connectionism and have a background in
one or more of the following: computing science, psychology,
mathematics, physics.
Starting salary for each post will be on the 1A scale for research staff
(up to \pounds 18165 pa). Both posts are scheduled to start as soon as
possible in 1991. Application forms and further particulars for both
posts can be obtained from The Director of Personnel Services, College
Gate, St Andrews University, St Andrews, Fife, KY16 9AJ, to whom
completed applications forms together with a CV should be submitted to
arrive no later than November 30th 1990.
Further information can be obtained informally from:
(Post 1) Dr Tim Jordan at St Andrews (tel.0334 76161, ext 7234)
(Post 2) Dr Leslie Smith at Stirling (tel.0786 67435, direct line)
Previous applicants for these posts need not re-apply.
Both Universities operate an Equal Opportunities Policy.
------------------------------
Subject: Talk at American University
From: masud cader <CADER%AUVM.BITNET@CUNYVM.CUNY.EDU>
Date: Fri, 12 Oct 90 02:15:55 -0400
ANNOUNCEMENT:
Seminar Series
Department of Computer Science and Information Systems
The American University
Paul J. Werbos
National Science Foundation
Neuro-control and Fuzzy Logic: Connections and Designs
2:00 pm Monday, October 29, 1990
LOCATION: To Be Announced
Astract: This presentation will focus on application of artificial neural
networks to control tasks. Concentration will be on contrasting Fuzzy
Logic with the interpolation power of current neural architectures, as
well as integrating fuzzy logic and neural systems. This discussion will
take place in the context of control theory. This talk can be considered
as a follow-up to Dr. Werbos' talk at IJCNN-WASH-90.
DIRECTIONS:
To get to American University (Washington DC) by public transportation,
there are many alternatives:
1. If you are coming from Downtown (DC) you can take the bus N2,
or N6 (Friendship Heights direction) at Dupont Circle.
2. If you are taking the metro (underground), please take the red line
to Tenleytown. From there, you can take M4 city bus or the AU shuttle
bus, or walk along nebraska avenue (about 15 min to walk to AU).
3. You can also stop at Friendship Heights, and then
take the N2, N4, or N6.
If you are driving a car, please park at the METROPOLITAN MEMORIAL UNITED
METHODIST CHURCH. American University is located at the intersection of
Massachusetts Ave (NW), and Nebraska Ave.
The Department of Computer Science and Information Systems is located in
Clark Hall, 4400 Massachusetts Ave., NW, Washington, DC. 20016. Call
202/885-1470 for further information, or send e-mail.
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End of Neuron Digest [Volume 6 Issue 61]
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