neuron-request@HPLMS2.HPL.HP.COM ("Neuron-Digest Moderator Peter Marvit") (08/18/90)
Neuron Digest Friday, 17 Aug 1990
Volume 6 : Issue 49
Today's Topics:
Re: universe and intelligence
Really smart systems
Re: Really smart systems
Re: Really smart systems
tasks for reinforcement learning
the dumb universe
Help for RTRL?
Re: Help for RTRL?
PYGMALION Overview
NN-definition Language
Re: NN-definition Language
Last Call for Papers for AGARD Conference
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------------------------------------------------------------
Subject: Re: universe and intelligence
From: arti6!chen@relay.EU.net (Chung-Chih Chen)
Date: Wed, 15 Aug 90 12:40:22 +0100
Concerning the reply from Douglas Danforth:
>Intelligence is in the mind of the intelligent AND why do you consider
>intelligence significant?
What I really mean in my last message can be explained more clearly as
follows: According to the inflationary model of the universe (see, for
example, the article by A. Guth & P. Steinhardt in the book edited by P.
Davies), the universe could have been created from virtually nothing (so
the universe may be the ultimate free lunch!). If the seemly very
complex behaviors of the current universe came from nothing, then can we
imagine that we may build a universal model of our brain (or neural
network) which will produce intelligent behaviors from nothing?
Nolfi et al. showed that complex and apparently purposeful behavior can
arise from random variation in networks. In some way they have shown
that intelligence can come from nothing (or evolution).
I hope this will clarify my idea.
%E P. Davies
%B The New Physics
%I Cambridge University Press
%D 1989
%A S. Nolfi
%A J. L. Elman
%A D. Parisi
%T Learning and Evolution in Neural Networks
%J CRL Technical Report 9019
%I Center for Research in Language, University of California
San Diego
%D July 1990
------------------------------
Subject: Really smart systems
From: kingsley@hpwrce.HP.COM (Kingsley Morse)
Organization: Ye Olde Salt Mines
Date: 14 Aug 90 21:05:25 +0000
If we aspire to making truly intelligent machines, our algorithms must
scale up well to large training sets. In other words, the computational
complexity of our algorithms must accomodate many training patterns and
many dimensions. You may have heard of the "curse of dimensionality". If
the computational complexity of our algorithms grows slowly, then we can
train with more patterns and dimensions to get a smarter system. On the
other hand, if the computation required grows explosively as the number
of training patterns or dimensions are increased, then even astoundingly
fast hardware won't help.
So, our challange is to find algorithms which scale up well to large
problems, so we can make really smart systems. Listen........... I'll
post some computational complexity figures for some common algorithms. If
you add to (correct?) these, please do. The terminology that I propose is
that linear computational complexity is better than polynomial which is
better than exponential. Furthermore, the algorithms' scalability must be
measured with respect to learning, recall, patterns, and dimensions.
Algorithm Learning Recall
patterns dimensions patterns dimensions
------------------------------------------------------------
Backprop polynomial ? independent linear
Nearest
neighbor linear linear linear linear
Cart nlogn exponential independent ?
This leads me to believe that nearest neighbor algorithms work better for
learning a lot because they can be trained faster.
Any contributions to this list are welcome.
------------------------------
Subject: Re: Really smart systems
From: mcsun!ukc!reading!minster!russell@uunet.uu.net
Organization: Department of Computer Science, University of York, England
Date: 17 Aug 90 11:21:56 +0000
Well, if you want tuppeny-worth's thrown in, here's mine.
Backprop has NO guarenteed convergence, therefore to quote computational
complexity is misleading. It may be possible to give an "average case"
complexity figure, but this is so dependent on the initial weight
settings as to be not too much use. Tesauro and Janssens report
empirical results between learning time and predicate order (q) of
patterns. The net has q inputs, 2q nodes in the first layer, fully
connected, and 1 output node. The task set is the parity function on the
q bits. Leaning times in b-p scale as approx. 4^q. The task set is of
size 2^q so the training time is about 2^q. Conclusion is that
empirically learning time is of exponential order.
The perceptron scales as exponential in input size (Hampson and Volper
1986).
(See Neural Network Design and the Complexity of Learning by Judd for
more.....I aint read it all yet, but it's interesting so far.....)
I confess to not understanding quite what "Cart" means in the above table -
I can make an educated guess, however.....
To add another algorithm to the list, the ADAM system, based on the
Willshaw net (i.e. a distributed associative memory) (Austin 1987) has
complexity as follows:
Algorithm Learning Recall
patterns dimensions patterns dimensions
------------------------------------------------------------
ADAM linear linear(quadratic) indep. linear(nlogn)
brackets refer to abnormal, but permissible, parameterisation. (Beale
1990).
Note that recall may take longer than teaching - but also note that the
realm of use of ADAM means that the multiplicative constants in front of
the order terms are extremely small.
Russell.
------------------------------
Subject: Re: Really smart systems
From: usenet@tut.cis.ohio-state.edu (usenet news poster)
Organization: National Library of Medicine, Bethesda, Md.
Date: 17 Aug 90 21:19:11 +0000
>From a separate communiction with km:
CART means "Classification and Regression Tree". It is similar to ID3,
and here's how it works. The training vectors are used to "grow" a
decision tree. The decision tree can be used catagorize new inputs.
OK, let me add a couple more cents to the pot. Classifications need to
consider both the computational complexity and the storage requirements.
Algorithms like nearest neighbor, CART? and ADAM? require that the
complete training set be stored for recall while a perceptron needs
storage of the order #inputs+#outputs, and a single hidden layer fully
connected neural net needs (#inputs+#output)*#hidden_nodes.
A second consideration is will the algorithm generalize? Ie., will it
make use of information from more than one input pattern to formulate an
output.
So at the risk of grossly misquoting and being flamed horribly let me
reorder the classification in terms of Np=#patterns, Ni=#inputs,
No=#outputs, and Nh=#hidden nodes:
Algorithm Learning time Recall time Storage Generalizes
-----------------------------------------------------------------------------
Perceptron No*(x^Ni) No*Ni No*Ni limited
Neural Net (1 hidden layer) Nh*(Ni+No) Nh*(Ni+No) yes
Backprop x^(Nh*(Ni+No)) ?
Conjugate gradient (Nh*(Ni+No))^3 - locally
Monte Carlo x^(Nh*(Ni+No)) ?
Nearest neighbor (Ni+No)*Np Ni*Np Np*(Ni+No) no
Class & Reg. Tree Ni*(Np log Np) Ni*log Np Np*(Ni+No) no
(CART) + (Ni+No)*Np ? + Ni*log Np
Adaptive Memory Np? ? Np*(Ni+No)? no?
(ADAM)
David States
------------------------------
Subject: tasks for reinforcement learning
From: finton@ai.cs.wisc.edu (David J. Finton)
Organization: U of Wisconsin CS Dept
Date: 15 Aug 90 17:28:08 +0000
I'm looking for good demonstration tasks for my reinforcement-learning
algorithm:
(1) Are there any good examples of real-world tasks which require
reinforcement learning -- where supervised techniques such as back-prop
would be unsuitable?
(2) Are there any studies which compare performance of reinforcement
learning systems with standard techniques (eg, back-prop, ID3) on such
tasks?
(3) Are there available standard data sets for such tasks?
(4) Are there studies comparing reinforcement learning on with back-prop
on standard back-prop tasks?
David Finton
------------------------------
Subject: the dumb universe
From: Stephen Smoliar <smoliar@vaxa.isi.edu>
Date: Wed, 15 Aug 90 17:38:37 -0700
There is no reason to assume that intelligence on the part of the
universe is prerequisite to it giving rise to intelligent systems. That
is the whole point of THE BLIND WATCHMAKER. It is also the point of THE
SOCIETY OF MIND. Minsky's argument is that you can build an intelligent
system from lots of little components, each of which is far to simple to
be, in itself, intelligent. Neurons are an example of such little
components.
------------------------------
Subject: Help for RTRL?
From: coms2146@waikato.ac.nz (Alistair Veitch, University of Waikato, New Zealand)
Organization: University of Waikato, Hamilton, New Zealand
Date: 16 Aug 90 03:54:44 +0000
Has anybody out there worked with Williams and Zipsers "Real-time
recurrent learning algorithm"? [Connection Science, Vol 1, No 1].
We are currently trying to implement this algorithm, but have run into
some problems. We've got it to run succesfully on the various XOR
problems described, the "ab" problem (recognise the first "b" after an
"a") and the oscillation problems. What we can't seem to achieve is
success for the Turing machine problem. As this is perhaps the major
result of the paper, it seems important to duplicate it to reassure
ourselves that everything is correct. Has anyone else had success/failure
with this problem? If success, would it be possible to post your source?
(We think we've got it right, but...)
Alistair Veitch Phone: +64 71 562889 ext. 8768
Internet: coms2146@waikato.ac.nz +64 71 562388 (home)
SNAIL: Computer Science Dept, University of Waikato, Hamilton, New Zealand
------------------------------
Subject: Re: Help for RTRL?
From: ins_atge@jhunix.HCF.JHU.EDU (Thomas G Edwards)
Organization: The Johns Hopkins University - HCF
Date: 16 Aug 90 19:38:09 +0000
In article <1243.26cac1c4@waikato.ac.nz> coms2146@waikato.ac.nz (Alistair Veitch, University of Waikato, New Zealand) writes:
>Has anybody out there worked with Williams and Zipsers "Real-time recurrent
>learning algorithm"? [Connection Science, Vol 1, No 1].
I haven't actually implemented this algorithm, but I have heard that it
is important to use the "Teacher Forcing" method they discuss to learn
difficult problems.
You might also want to look at J. Schmidhuber, "Making the World
Differentiable: On using supervised learning fully-recurrent networks for
dynamic reinforcement learning and planning in non-stationary
environments", FKI Report 125-90, Technische Univeritat Munchen, 1990. A
pole-balancer is trained by reinforcement learning (i.e. apply pain when
the pole is dropped).
And to explain why gradient-descent methods will probably not give you
reasonable temporal learning see J. Schmidhuber, "Towards compositional
learning with dynamic neural networks", FKI Report 129-90, TUM, April
1990.
He explains that gradient-descent-only methods must take into account
training learned during all past time steps when dealing with a new
problem. For "toy" temporal learning problems, this is not a big
impediment. For "serious" temporal learning problems, dynamic neural
systems must develop methods of breaking goals down into subgoals, most
of which have already been learned, some of which need to be developed by
gradient-descent. In this way, only small problems are trained by
gradient-descent, and they are used by the system combinatorially to
allow the network-of-networks to solve real problems by
"divide-and-conquer" methods. The research is very fresh into this area,
and I think in about a year there will be a move away from naive
implementations of gradient-descent learning in both stationary and
temporal learning and a move towards connectionist compositional learning
(Cascade-Correlation is a simple example of this).
-Thomas Edwards
------------------------------
Subject: PYGMALION Overview
From: M.Azema@cs.ucl.ac.uk
Date: Fri, 17 Aug 90 10:01:52 +0100
In response to requests about the PYGMALION environment, (with some
delays) here is an overview:
PYGMALION Overview:
-------------------
The ESPRIT II PYGMALION project is intended to provide a focus for neural
computing research within the European Community. PYGMALION aims to
promote the application of neural networks by European industry, and to
develop European "standard" computational tools for programming and
simulation of neural networks.
The design philosophy of the PYGMALION neural programming environment is
twofold. Firstly, to provide an "open" programming environment - a
rudimentary "platform" - that can be easily extended and interfaced to
other tools. For this reason the core of the environment is X-windows, C
and C++; running on a colour workstation. Secondly, to provide
"portable" neural network applications, so that trained and partially
trained networks can be easily moved from machine to machine. For this
reason the (partially) trained neural network applications are specified
in a subset of C; essentially a C data structure.
The environment comprises 5 major parts:
Graphic Monitor, the graphical software environment for controlling the
execution and monitoring of a neural network application simulation. This
includes a simulation command language for setting up a simulation,
monitoring its execution, interactively changing values, and saving a
trained network.
Algorithm Library, the parameterised library of common neural networks,
written in the high level language and providing the user with a number
of validated modules for constructing applications.
High Level Language N, the object-oriented programming language for
defining, in conjunction with the algorithm library, a neural network
algorithm and application, by describing the network topology and its
dynamics.
Intermediate Level Language nC-code, the low level machine independent
network specification language for representing the partially trained or
trained neural network applications, a format analogous to P-code for
PASCAL systems.
Compilers to the target UNIX-based workstations and parallel
Transputer-based machines.
Availability of the software:
-----------------------------
A preliminary version of the PYGMALION software is now available (free of
charge). If you would like more information please contact :
Mike Hewetson
Department of Computer Science
University College London
Gower Street
London
WC1E 6BT
Voice: +44 (0) 71 387 7050 ext 3708
Fax: +44 (0) 71 387 1397
Email: M.Hewetson@cs.ucl.ac.uk
------------------------------
Subject: NN-definition Language
From: ethz!neptune!brain!thalmann@uunet.uu.net (Laura Thalmann)
Organization: Department for Informatik,Universitat Zurich-Irchel
Date: 17 Aug 90 11:23:54 +0000
Hi neural-experts,
This is a presentation of (yet another) neural network implementation, a
NN-definition language:
Condela means CONnection DEfinition LAnguage and it is a high level
programming language, specifically designed for the development and
modeling of neural network applications. It is a procedural and general
purpose language, that allows parallelism via the concept of selections,
i.e. groups of units or connections to which actions can be applied.
Units and connections can be created dynamically at any point in the
program flow. The parallelism expressible in Condela-3 is independent of
the underlying Hardware. Condela-3 is easy to teach, as it has few
language constructs, yet allows the expression of arbitrary network
topologies and learning paradigms due to its powerful statements and its
two levels of abstraction. It is easily portable to other operating
systems, its open design allows simple interfacing to existing
applications.
The following sample program demonstrates the classical XOR learning
problem using error back propagation.
1 TOPOLOGY
2 xor = LAYER input OF FIELD[2]; END;
3 LAYER hidden OF FIELD[2]; END;
4 LAYER output OF FIELD[1]; END;
5 VAR p : NETWORK OF xor;
6
7 PROCEDURE main();
8 VAR output_vec, input_vec : VECTOR;
9 input_layer, hidden_layer, output_layer : USEL;
10
11 BEGIN
12 CREATE p;
13 input_layer := { p.input[0..1] };
14 hidden_layer := { p.hidden[0..1] };
15 output_layer := { p.output[0] };
16 CONNECT input_layer TO hidden_layer INIT random();
17 CONNECT hidden_layer TO output_layer INIT random();
18 LOOP 1000000 TIMES
19 get_input(input_vec, output_vec);
20 input_layer : out := input_vec;
21 APPLY feed_forward() TO hidden_layer;
22 APPLY feed_forward() TO output_layer;
23 APPLY back_propagate_out(output_vec) TO output_layer;
24 APPLY back_propagate_hid() TO hidden_layer;
25 END;
26 END;
It has a 2 layered implementation that allows the "abstract" definition
of a neural network topology and behavior and a "concrete" implementation
in C. This compiler (implemented with lex and yacc) translates the
Condela-source to C and therefore allows simple interfacing to other
existing neural network simulation systems. I appreciate any comments.
-Nick.
,----------------------------------------------------,
| Nikolaus Almassy almassy@ifi.unizh.ch /
| University of Zurich-Irchel Tel:+41-1-257 43 15 /
| Department of Informatik Fax:+41-1-257 43 43 /
| Winterthurerstr. 190 CH-8057 SWITZERLAND /
`-----------------------------------------------'
------------------------------
Subject: Re: NN-definition Language
From: van-bc!ubc-cs!kiwi!snider@ucbvax.Berkeley.EDU (Duane Snider)
Organization: Microtel Pacific Research Ltd., Burnaby, B.C., Canada
Date: 17 Aug 90 17:45:19 +0000
> [[CONDELA]] It is a procedural and
^^^^^^^^^^
>general purpose language, that allows parallelism via the concept of
^^^^^^^^^^^^^^^^^^^^^^^^
It appears CONDELA isn't doing anything more than a programming language
like C++ could handle.
Are you sure that another language is necessary in this field, yet?
Duane Snider
snider@mpr.ca
------------------------------
Subject: Last Call for Papers for AGARD Conference
From: nelsonde%avlab.dnet@wrdc.af.mil
Date: Mon, 13 Aug 90 10:10:04 -0400
Subject: Last Call for Papers for AGARD Conference
We are extending the deadline for the abstracts for the papers to be
presented at the AGARD conference until 21 September 1990.
In case you have lost the Call for Papers, it is again attached to this
message.
Your consideration is greatly appreciated.
--Dale
AGARD
ADVISORY GROUP FOR AEROSPACE RESEARCH AND DEVELOPMENT
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AVP/46 2 APRIL 1990
CALL FOR PAPERS
for the
SPRING, 1991 AVIONICS PANEL SYMPOSIUM
ON
MACHINE INTELLIGENCE FOR AEROSPACE ELECTRONICS SYSTEMS
to be held in
LISBON, Portugal
13-16 May 1991
This meeting will be UNCLASSIFIED
Abstracts must be received not later than 31 August 1990.
Note: US & UK Authors must comply with National Clearance Procedures
requirements for Abstracts and Papers.
THEME
MACHINE INTELLIGENCE FOR AEROSPACE ELECTRONICS SYSTEMS
A large amount of research is being conducted to develop and apply
Machine Intelligence (MI) technology to aerospace applications.
Machine Intelligence research covers the technical areas under the
headings of Artificial Intelligence, Expert Systems, Knowledge
Representation, Neural Networks and Machine Learning. This list is
not all inclusive. It has been suggested that this research will
dramatically alter the design of aerospace electronics systems
because MI technology enables automatic or semi-automatic operation
and control. Some of the application areas where MI is being
considered inlcude sensor cueing, data and information fusion,
command/control/communications/intelligence, navigation and guidance,
pilot aiding, spacecraft and launch operations, and logistics support
for aerospace electronics. For many routine jobs, it appears that MI
systems would provide screened and processed ata as well as
recommended courses of action to human operators. MI technology will
enable electronics systems or subsystems which adapt or correct for
errors and many of the paradigms have parallel implementation or use
intelligent algorithms to increase the speed of response to near real
time.
With all of the interest in MI research and the desire to expedite
transition of the technology, it is appropriate to organize a
symposium to present the results of efforts applying MI technology to
aerospace electronics applications. The symposium will focus on
applications research and development to determine the types of MI
paradigms which are best suited to the wide variety of aerospace
electronics applications. The symposium will be organizaed into
separate sessions for the various aerospace electronics application
areas. It is tentatively proposed that the sessions be organized as
follows:
SESSION 1 - Offensive System Electronics (fire control systems, sensor
cueing and control, signal/data/information fusion, machine
vision, etc.)
SESSION 2 - Defensive System electronics (electronic counter
measures, radar warning receivers, countermeasure
resource management, situation awareness, fusion, etc.)
SESSION 3 - Command/Control/Communications/Intelligence - C3I (sensor
control, signal/data/information fusion, etc.)
SESSION 4 - Navigation System Electronics (data filtering, sensor
cueing and control, etc.)
SESSION 5 - Space Operations (launch and orbital)
SESSION 6 - Logistic Systems to Support Aerospace Electronics (on and
off-board systems, embedded training, diagnostics and
prognostics, etc.)
GENERAL INFORMATION
This Meeting, supported by the Avionics Panel will be held in Lisbon,
Portugal on 13-16 May 1991.
It is expected that 30 to 40 papers will be presented. Each author
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MACHINE INTELLIGENCE FOR AEROSPACE ELECTRONICS SYSTEMS
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2. Attach a copy of your abstract to these forms before they are
mailed. US Authors must comply with ATTACHMENT 1 requirements.
a. Probable Title Paper: __________________________________________
_____________________________________________________________________
b. Paper most appropriate for Session # ____________________________
c. Full Name of Author to be listed first on Programmee,
including Courtesy Title, First Name and/or Initials, Last
Name & Nationality.
d. Name of Organization or Activity: _______________________________
_____________________________________________________________________
e. Address for Return Correspondence: Telephone Number:
__________________________________ __________________
__________________________________ Telefax Number:
__________________________________ __________________
__________________________________ Telex Number:
__________________________________ __________________
f. Names of Co-Authors including Courtesy Titles, First Name and/or
Initials, Last Name, their Organization, and their nationality.
_________________________________________________________________
_________________________________________________________________
_________________________________________________________________
_________________________________________________________________
_________________________________________________________________
__________ ____________________
Date Signature
DUE NOT LATER THAN 21 SEPTEMBER 1990
------------------------------
End of Neuron Digest [Volume 6 Issue 49]
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