Preprints Available

[Brian Yamauchi]

The following papers are available via anonymous ftp from
yuggoth.ces.cwru.edu:

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Sequential Behavior and Learning in Evolved Dynamical Neural Networks

	       Brian Yamauchi(1) and Randall Beer(1,2)

	  Department of Computer Engineering and Science(1)
		       Department of Biology(2)
		   Case Western Reserve University
			 Cleveland, OH 44106

      Case Western Reserve University Technical Report CES-93-25

	  This paper will be appearing in Adaptive Behavior.

			       Abstract

This paper explores the use of a real-valued modular genetic algorithm
to evolve continuous-time recurrent neural networks capable of
sequential behavior and learning.  We evolve networks that can
generate a fixed sequence of outputs in response to an external
trigger occurring at varying intervals of time.  We also evolve
networks that can learn to generate one of a set of possible sequences
based upon reinforcement from the environment.  Finally, we utilize
concepts from dynamical systems theory to understand the operation of
some of these evolved networks.  A novel feature of our approach is
that we assume neither an a priori discretization of states or time
nor an a priori learning algorithm that explicitly modifies network
parameters during learning.  Rather, we merely expose dynamical neural
networks to tasks that require sequential behavior and learning and
allow the genetic algorithm to evolve network dynamics capable of
accomplishing these tasks.

Files:

/pub/agents/yamauchi/seqlearn.ps.Z	Article Text (73K)
/pub/agents/yamauchi/seqlearn-fig.ps.Z	Figures (654K)

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    Integrating Reactive, Sequential, and Learning Behavior Using
		      Dynamical Neural Networks

	      Brian Yamauchi(1,3) and Randall Beer(1,2)

	  Department of Computer Engineering and Science(1)
		       Department of Biology(2)
		   Case Western Reserve University
			 Cleveland, OH 44106

    Navy Center for Applied Research in Artificial Intelligence(3)
		      Naval Research Laboratory
		      Washington, DC 20375-5000

 This paper has been submitted to the Third International Conference
		 on Simulation of Adaptive Behavior.

			       Abstract

This paper explores the use of dynamical neural networks to control
autonomous agents in tasks requiring reactive, sequential, and
learning behavior.  We use a genetic algorithm to evolve networks that
can solve these tasks.  These networks provide a mechanism for
integrating these different types of behavior in a smooth, continuous
manner.  We applied this approach to three different task domains:
landmark recognition using sonar on a real mobile robot,
one-dimensional navigation using a simulated agent, and
reinforcement-based sequence learning.  For the landmark recognition
task, we evolved networks capable of differentiating between two
different landmarks based on the spatiotemporal information in a
sequence of sonar readings obtained as the robot circled the landmark.
For the navigation task, we evolved networks capable of associating
the location of a landmark with a corresponding goal location and
directing the agent to that goal.  For the sequence learning task, we
evolved networks that can learn to generate one of a set of possible
sequences based upon reinforcement from the environment.  A novel
feature of the learning aspects of our approach is that we assume
neither an a priori discretization of states or time nor an a priori
learning algorithm that explicitly modifies network parameters during
learning.  Instead, we expose dynamical neural networks to tasks that
require learning and allow the genetic algorithm to evolve network
dynamics capable of accomplishing these tasks.

Files:

/pub/agents/yamauchi/integ.ps.Z		Complete Article (233K)

If your printer has problems printing the complete document as a
single file, try printing the following two files:

/pub/agents/yamauchi/integ-part1.ps.Z	Pages 1-8 (77K)
/pub/agents/yamauchi/integ-part2.ps.Z	Pages 9-11 (147K)

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 On the Dynamics of a Continuous Hopfield Neuron with Self-Connection

			     Randall Beer

	    Department of Computer Engineering and Science
			Department of Biology
		   Case Western Reserve University
			 Cleveland, OH 44106

      Case Western Reserve University Technical Report CES-94-1

	 This paper has been submitted to Neural Computation.

Continuous-time recurrent neural networks are being applied to a wide
variety of problems.  As a first step toward a comprehensive
understanding of the dynamics of such networks, this paper studies the
dynamical behavior of their basic building block: a continuous
Hopfield neuron with self-connection.  Specifically, we characterize
the equilibria of this model neuron and the dependence of those
equilibria on the parameters.  We also describe the bifurcations of
this model and derive very accurate approximate expressions for its
bifurcation set.  Finally, we indicate how the basic theory developed
in this paper generalizes to a larger class of related model neurons.

File:

/pub/agents/beer/CTRNNDynamics1.ps.Z	Complete Article (233K)

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FTP instructions:

To retrieve and print a file (for example: seqlearn.ps), use the
following commands:

unix> ftp yuggoth.ces.cwru.edu
Name: anonymous
Password: (your email address)
ftp> binary
ftp> cd /pub/agents/yamauchi (or cd /pub/agents/beer for CTRNNDynamics1.ps.Z)
ftp> get seqlearn.ps.Z
ftp> quit
unix> uncompress seqlearn.ps.Z
unix> lpr seqlearn.ps

(ls doesn't currently work properly on our ftp server.  This will be fixed
soon, but in the meantime, these files can still be copied, even though
they don't appear in the directory listing.)

_______________________________________________________________________________

Brian Yamauchi			Case Western Reserve University
yamauchi at alpha.ces.cwru.edu	Department of Computer Engineering and Science
_______________________________________________________________________________