neuron-request@HPLMS2.HPL.HP.COM ("Neuron-Digest Moderator Peter Marvit") (09/28/90)
Neuron Digest Thursday, 27 Sep 1990
Volume 6 : Issue 56
Today's Topics:
Abstracts for MIND conference in October
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Subject: Abstracts for MIND conference in October
From: lindahl@evax.arl.utexas.edu (Charlie Lindahl)
Date: Fri, 21 Sep 90 16:17:12 -0500
Below are the abstracts for the presentations for the MIND conference
October 4-7 in Dallas.
Charlie S. Lindahl
Automation and Robotics Research Institute
University of Texas at Arlington
Internet EMAIL: lindahl@evax.arl.utexas.edu
Oral Presentations
DATA REPRESENTATION, NEURAL NETWORKS, AND HYBRID COMPUTATION
James Anderson
Dept. of Cognitive and Linguistic Science
Brown University
The human brain seems to be a hybrid computer, partly a highly efficient,
highly evolved, massively parallel "intuitive" and "perceptual"
processor, and partly a slow and inaccurate serial symbol manipulator.
Neural network models seem compatible with the massively parallel
hardware of the nervous system, and are probably the best current model
for the parallel part of the hybrid. Traditional symbol based artificial
intelligence has usually based its models on the presumed properties of
the symbol manipulator. Potentially, the strengths and weaknesses of the
two parts of the hyubrid compelment each other nicely, and ultimately a
computer could make good use of both strategies simultaneously. However,
the coupling between them in humans is still uneasy, perhaps because this
novel combination is biologically recent.
Let us discuss two examples of distributed hybrid computation and its
associated problems of data representation as examples.
First, we will discuss a network for determining large scale object
motion from small scale, ambiguous local motion signals suggested by
Margaret Sereno in her Ph. D. thesis. single units early in the visual
system have small receptive fields, somewhat like looking at the visual
system through a bunch of straws. The visual system puts together
information from many sensors to give rise to perception of the large
scale structure in the visual world. Determination of object motion from
local motion singals is one well studied example of such a computation.
The task of detecting object motion from local motion is known as the
aperture problem. A very simple neural net can combine local information
about edge movements to determine larger scale object motion.
Second, we have recently looked at learning simple arithmetic with a
neural network. We might expect neural networks to perform arithmetic
poorly, because humans find it hard, and this seems to be the case.
Arithmetic learning has been studied for many years. It can be viewed as
only a set of arbitrary relations between symbols, for example,
memorization of flashcard information. But there are richer meanings
attached to the numbers that are being added or multiplied. Experiments
show that humans seem to possess an internal representation of numbers
related to their magnitudes. Neural network simulations obtained best
results when formally redundant hybrid representations with both
magnitude information and arbitrary number codes were used. Data on
human error patterns in arithmetic learning indicates most errors are
similar in magnitude to the correct answers. The simulations show that
the model estimates values that it has not learned, signs of a
rudimentary "intuition" process and even the errors observed in the
simulations showed a similar pattern.
INFORMATION REPRESENTATION BY PATTERNS OF EVENT-RELATED
POTENTIALS IN HUMANS
Jean-Paul Banquet
Hospital de la Salpetriere, Paris
and
Center for Adaptive Systems
Boston University
(To be added)
AN ANALOGY-BASED REASONING APPROACH TO THE CONNECTIONS VERSUS
SYMBOLS DEBATE
John Barnden
Dept. of Computer Science
New Mexico State University
Symbol manipulation as used in traditional artificial intelligence has
been criticized by neural net researchers for being excessively
inflexible and sequential. On the other hand, the application of
connectionist techniques to the types of high-level cognitive processing
studied in traditional artificial intelligence presents major problems as
well. I claim that a promising way out of this impasse is to build
connectionist models that implement massively parallel analogy-based
reasoning. This is because analogy-based reasoning avoids many of the
rigidity problems leveled at traditional artificial intelligence.
Although the type of system proposed is partially a version of
implementational connectionism, it can also make good use of
connectionist techniques for approximate matching and associative
retrieval. I am accordingly modifying an existing connectionist system
(Conposit), which implements standard rule-based reasoning, into a
massively parallel case-based reasoning version. However, the talk will
concentrate on motivations and justifications, and will not go into the
new system itself in any detail.
(This work has been supported in part by grant AFOSR-88-0215 from the Air
Force Office of Scientific Research and grant NAGW-1592 under the
Innovative Research Program of the NASA Office of Space Science and
Applications.)
USING GENETIC ALGORITHMS IN THE DESIGN AND CONSTRUCTION OF
ARTIFICIAL NEURAL NETS
Warren Bean
Texas Instruments, Inc.
Genetic algorithms provide a means for discovering efficient topologies,
training set partitions, and parameter settings for use in the design and
construction of neural nets. In this talk I will illustrate some of the
ways genetic algorithms can be used to design or optimize the process of
finding good neural network solutions to certain problems. I will review
some of the hybrid approaches in which a genetic algorithm is combined
with a neural network to solve problems that might have been difficult to
solve by just one technique alone. I will compare a Holland classifier
algorithm in which production rules are discovered via a genetic
algorithm with a specific implementation of a neural net. By this
comparison I will try to highlight the features each approach brings to
bear in solving problems in adaptation.
THE BRAIN AS A COMPUTER -- OR IS IT?
Claude Cruz
Plexus Systems, Inc.
The operation of nervous systems has been likened to many things in the
past. To Galen, the nervous system was a pneumatic network. Descartes
thought of the brain as a fantastic clockwork. More recently, the human
brain has been compared to a telephone exchange. In this paper, we will
view the operation of an advanced nervous system in light of the
"paradigm du jour": the computer.
This comparison will be undertaken from a functional perspective. That
is, we will focus on the information-processing properties and
capabilities of both computers and nervous systems. Little will be said
of the possible ties to neurobiology, apart from a commonality of some
high-level operating principles. Likewise, we will not discuss the
circuit-level details of computer implementation approaches.
In keeping with the title of this Workshop, we will delineate the
fundamental operations performed by a computer. We will explore what the
notion of representation means in the context of a computer, and in the
context of a nervous system. We will see that both computers and nervous
systems may be viewed as pattern- processing mechanisms, though of rather
different kinds. Possible analogous mechanisms in nervous systems will
be identified, and we will also note differences in how computers
operate. These considerations will lead to a proposal that we
distinguish between "symbolic" processing systems (such as computers) and
"semantic" processing systems (nervous systems).
QUANTUM NEURODYNAMICS AND THE REPRESENTATION OF KNOWLEDGE
Robert Dawes
Martingale Research Corporation
Quantum Neurodynamics (QND) is the theoretical basis for a neurocomputing
architecture which implements powerful stochastic filtering methods.
Unlike the vast majority of existing artificial neural system
architectures, the QND architecture is inherently designed to comprehend
nonstationary observations of nonlinear dynamical systems. Simulations
have shown that a QND observer can simultaneously track multiple
accelerating targets in simulated imagery and can solve the "broom
balancing" problems from well into its nonlinear region.
This talk explores the implications of the QND model for knowledge
representation in biological systems. We show how QND can model
representational dysfunctions resulting in multiple personality, and we
introduce models for the reification of synthetic inference and deductive
logic.
AN EXPERT SYSTEM FOR THE DIAGNOSIS OF PAPULOSQUAMOUS SKIN DISORDERS
USING ID3 AND NEURAL NETS
Jonathan Fenner, Kyle Morris, and Lynn Peterson
Dept. of Computer Science
University of Texas at Arlington
YoungOhc Yoon
Dept. of Computer and Information Science
Southwestern Missouri State University
This paper presents the results of the development of an expert system to
assist medical students in diagnosing papulosquamous skin disorders.
These disorders form a broad category of skin disease which is fairly
common, frequently confused, and have widely varying etiologies.
This expert system includes two methodologies running in parallel: a
connectionist approach, and a symbolic learning system based on ID3.
This paper will compare both approaches in terms of accuracy, learning
time, and sensitivity to learning set size.
TOWARD A MARKOV RANDOM FIELD MODEL OF STORY COMPREHENSION
AND RECALL
Richard Golden
School of Human Development
University of Texas at Dallas
David Rumelhart
Department of Psychology
Stanford University
Understanding and recalling a story is formally viewed as an optimal
control problem. In particular, the reader's mental state is modelled as
a collection of d binary-valued features. This state is represented as a
point in a d+1-dimensional situation state space by the state vector
X(t). Thus, a sequence of points (or trajectory) in situation state
space is assumed to reference some space-time process in the physical
world.
The reader's knowledge of the world consists of trajectories
(i.e.,sequences of situations) in situation state space. The knowledge
base is compactly represented by a subjective probability distribution of
the form p(X(1), ..., X(T)~W) which expresses the reader's belief that
trajectory X(1), ..., X(T) will occur given that the reader is in memory
state W. The reader also possesses some unmodifiable "long-term" beliefs
about W which are embodied in the prior distribution p(W).
A story is modelled as a set, D, of constraints in situation state space.
In particular, let D be a particular description of a story. The
reader's belief that D is a description of a trajectory X(1), ..., X(T)
is modelled by the subjective probability distribution P(D~X(1), ...,
X(T)).
The comprehension and recall processes are assumed to operate as follows.
The reader computes the "most believable" situation state space
trajectory given a story description D* by maximizing the likelihood
function p(D*~X(1), ..., X(T))p(X(1), ..., X(T)~W)p(W) with respect to
X(1), ..., X(T) and W. In the process of this computation the belief
that feature xi of situation X(t) is active, p(xi(t) = 1~W, ~xj~xi), is
also computed. Let ~c be the system comprehension threshold and ~r be
the system recall threshold where 0<~c<~r. If p(xi=1~W,
~xj(t)~xi(t))>~c, then xi(t) is "understood" by the system to be active
in situation X(t). If p(xi=1~W, ~xj(t)~xi(t))>~r, then xi(t) is defined
as "recalled" by the system.
Some preliminary simulations which illustrate the viability of this
approach are described. In the simulations, a story grammar analysis of
several short children's stories is used to derive the situation state
space representation. Next, a Markov random field modelling approach is
used to construct consistent computationally tractable probability
distributions through the used of meaningful independence assumptions.
Finally, the resulting probability distributions are optimized using a
gradient descent algorithm which is formally equivalent to a special type
of recurrent neural network algorithm.
KNOWLEDGE REPRESENTATION IN THE HUMAN VISUAL SYSTEM
William Hudspeth
Center for Brain Research and Info. Science
Radford University
Visual percepts and concepts are organized in a way that various objects
are classified according to similarities of attributes and meaning. This
property can be defined by a multidimensional metric, and visual stimuli
can be designed to assure classification by these principles. In the
present work, perceptual and conceptual classification stimulus sets were
used to obtain visual evoked potential recordings. The quantitative
relationships among the stimuli in each set were computed as an a priori
model of how humans would classify perceptual and conceptual stimulus
sets. This model predicted the relationship among visual evoked
potentials within each stimulus set with 99% accuracy.
The results of these studies show that the brain electrical recordings,
and their numerical indices, retained all of the information specified in
the perceptual and conceptual classification specified by the a priori
model. Thus, the human visual system provides a linear transfer function
for the relationships among stimulus objects according to their
attributes and meanings, showing that the electrical activity of the
human brain specifies the exact attributes of objects in the visual
world.
KNOWLEDGE AND THE STRUCTURE OF MIND
Samuel Leven
Center for Brain Research and Info. Science
Radford University
(To be added)
CONTINUOUS SYMBOL SYSTEMS: THE LOGIC OF CONNECTIONISM
Bruce MacLennan
Dept. of Computer Science
University of Tennessee, Knoxville
It has been long assumed that knowledge and thought are most naturally
represented as discrete symbol systems (calculi). Thus a major
contribution of connectionism is that it provides an alternative model of
knowledge and cognition that avoids many of the limitations of the
traditional approach. But what idea serves for connectionism the same
unifying role that the idea of a calculus served for the traditional
theories? It is the idea of a continuous symbol system.
This talk presents a preliminary formulation of continuous symbol systems
and indicates how they may aid the understanding and development of
connectionist theories. It begins with a brief phenomenological analysis
of the discrete and continuous; the aim of this analysis is to directly
contrast the two kinds of symbols systems and show their equal primacy.
Next, based on the phenomenological analysis and on other observations of
existing continuous symbol systems and connectionist models, I sketch a
mathematical characterizationn of these systems. Finally the talk turns
to some applications of the theory and to its implications for
decidability issues and the theory of computation.
NOVELTY MONITORING, METACOGNITION AND CONTROL IN A COMPOSITE
HOLOGRAPHIC ASSOCIATIVE RECALL MODEL (CHARM): IMPLICATIONS FOR
KORSAKOFF AMNESIA
Janet Metcalfe
Department of Psychology
Dartmouth College
This work derives from a technical problem in composite-trace distributed
models of human memory, and particularly in CHARM. The problem is of
interest because it appears that with certain kinds of organic brain
disorders, people experience a similar problem. Briefly, the composite
trace employed as a central construct in such models can become
catastrophically out of control. To solve the problem a pre-storage
'novelty-familiarity' monitor and a simple control procedure need to be
implemented. Experimental evidence converging on the idea that output
from such a monitor underlies people's metacognitive judgments of
'feeling of knowing' is outlined. Breakdown of the monitoring-control
mechanism is shown to produce Korsakoff-like symptoms in the model.
Impairments in feeling-of-knowing judgments and the failure to release
from proactive inhibition, both characteristic of Korsakoffs, are, thus,
attributed to monitoring-control failure rather than to deficits in the
basic memory system.
THE ADAPTRODE: AN ADAPTIVE SIGNAL PROCESSOR THAT CAN ENCODE
ASSOCIATIONS PROVIDES A NEW LEARNING MECHANISM FOR NEURAL NETWORKS
George Mobus
Department of Computer Science
University of North Texas
Learning rules in artificial neural networks have been formulated with an
underlying assumption that associativity is the primal feature of
learning phenomena. Thus all such rules attempt to form associations
either between an input signal and the output of the neural element (Hebb
Rule), between two adjacent input signals (Alkon's Local Interaction) or
between an output and a desired output (delta rule and derivatives).
Without debating the significance of associativity in learning, I assert
that information encoding based strictly on associativity loses
information. The assumption of associativity leas to highly constrained
rules for changing synaptic efficacy and tends to exclude time. The
Adaptrode is an adaptive signal processor that encodes information in
response to its experience in multiple time sdcales. Its behavior mimics
both synaptic junctions and the axonal hillock in a variety of neuronal
types. A simple "permission" gating scheme provides a means for
establishing temporal and spatial associations among Adaptrodes. Thus in
neuromimes using Adaptrode elements as synapses and thresholds, it it
possible to model Hebbian, Local Interaction and error minimization
learning. But in addition, associations can be established between
different Adaptrodes at different time scales. Short and long-term
potentiation and neuromodulated memory phenomena have been modeled. A
simple robot decision controller demonstrates conditioned associations
between conditionable and unconditionable stimuli.
REPRESENTING PROPOSITIONAL LOGIC IN INTERACTIVE SYMMETRIC NETWORKS
Gadi Pinkas
Dept. of Computer Science
Washington University, St. Louis
Interactive symmetric connectionist models, like the Hopfield model and
Boltzmann machine, use gradient descent to minimize energy functions. We
show an efficient way to translate a given propositional Well Formed
Formula (WFF) into an energy function that represents the WFF. The
energy function can then be implemented using a connectionist network
that minimizes it. The technique we use generates an energy function
such that the set of vectors that minimizes the function (to global
minima) is equal to the set of truth assignments (models) that satisfy
the WFF. The connectionist network can therefore be seen as performing a
search for a satisfying model.
We found an equivalence between the problem of satisfiability and the
problem of connectionist energy minimization. For every WFF we can find
a class of energy functions that represent the WFF and for every
connectionist energy function we can find a class of WFFs that are
represented by the function.
The algorithm to transform a WFF into a connectionist network produces a
network that may include hidden units and determines its exact topology
and weights. The network that is generated is efficient in the sense
that the number of hidden units created is linear in the length of the
original WFF and the fan-out of these hidden units is bounded by a
constant. We also show that by using sigma-pi units we cna eliminate
completely the need for any hidden units.
Given a set of possibly contradicting WFFs, the technique can be used to
find the maximal consistent subset of a set of beliefs. Each WFF may
have a penalty assigned to it that is effective if the WFF is not
satisfied. The gnerated network searches for a model that minimizes the
total penalty. In the special case where all the WFFs have the same
penalty, the system minimizes the cardinality of the subset of
unsatisfied WFFs. At equilbrium those WFFs that are satisfied by the
minimum penalty model are in the maximal consistent subset. Finding the
maximal consistent subset may be applied to certain non-monotonic
paradigms (Truth Maintenance Systems for example) where we can consider a
total order of preference among the beliefs. In such a system for
example, absolute logic rules should be preferred over defeasible rules
and assigned a higher penalty. Contradicting arguments compete among
themselves and those who win are the ones that cause the less damage in
the sense of total penalty or cardinality. Such connectionist
non-monotonic systems feature incremental updating of the knowledge so
that when we add to or update the knowledge base, we change the network
locally without the need for re-calculating it all over again. The same
method can also be used for tolerating possible observation errors by
ignoring those which contradict a consistent majority set.
Adding a little more sophistication, we can now implement a 3-value logic
propositional inference engine. We want the network to deduce "unknown"
when a proposition can be either "true" or "false" in two different
satisfying models of the maximal consistent subset. For the rest of the
poropstions the network is forced to deduce either "true" or "false."
The trick is to construct an energy function with only one global minimum
by introducing weak constraints that cause the network to prefer
"deducing unknown" as long as it does not violate any fo the 3-value
logic WFFs that are in the maximal consistent subset.
RECURSIVE DISTRIBUTED REPRESENTATIONS
Jordan Pollack
Dept. of Computer and Information Science
Ohio State University
A longstanding difficulty for connectionist modeling has been how to
represent variable-sized recursive data structures, such as trees and
lists, in fixed-width patterns. I present a connectionist architecture
which automatically develops compact distributed representations for such
compositional structures, as well as efficient accessing mechanisms for
them. Fixed-dimensional patterns which stand for the internal nodes of
finite sets of fixed-valence trees are devised through the recursive use
of back-propagation on three-layer auto-associative encoder networks.
The resulting representations are novel, in that they combine apparently
immiscible aspects of features, pointers, and symbol structures. They
form a bridge between the data structures necessary for high-level
cognitive tasks and the associative, pattern recognition machinery
provided by neural networks.
THE NEURODYNAMICS OF FRONTAL LOBE PROCESSING
Karl Pribram
Center for Brain Research and Info. Science
Radford University
Evidence will be presented that (a) the effects of amygdalectomy on
serial position, (b) the effects of hippocampectomy on primacy, and (c)
the effects of far frontal lobe resections on intralist interference
(relative recency) stem from inadequate perceptual processing at the time
of initial exposure to the list of items, the establishment of an
episode, the context that stabilizes further processing, and not to
effects on the trace of the sensory input. We have all experienced a
related phenomenon when we attempt to recite a poem or rehearse a melody:
should we be interrupted or fail, for the moment, to be able to continue
the recitation or rehearsal we often find it necessary to begin again at
the beginning of the entire poem or piece, or at least at the beginning
of a major section.
Murdock has developed a convolutional model that better handles these
serial position effects than does a matrix model (which deals more
readily with categorical processing of prototypes). Extensions of the
convolutional model by Smolensky (a dynamical theory) and Pribram (a
holonomic brain theory) account for other behavioral and experiential
phenomena linked to the functions of the frontolimbic forebrain.
Processing that leads to practical inference is one such function. A
mathematical model of the neurodynamics that can lead to inference will
be described.
EXPLORING CONNECTIONIST APPROACHES TO LEGAL DECISION MAKING
Wullianalur Raghupathi and Lawrence Schkade
Department of Information Systems
University of Texas at Arlington
Raju Bapi and Daniel Levine
Department of Mathematics
University of Texas at Arlington
In this exploratory paper, we discuss research in connectionism and
neurophysiology and examine the potential implications for understanding
and modeling legal decision making. Artificial Intelligence approaches
such as rule-based and case-based, while adequate for implementing
systems in specific legal domains, have proved insufficient for modeling
complex, large scale legal systems that assimilate a variety of inputs.
This paper contributes to three important goals: modeling legal decision
processes as special instances of human decision making,
conceptualization of intelligent, computer-based legal systems, and
moving toward integrated frameworks for research.
- -------------------------------------------------------------
FROM SIMPLE ASSOCIATIONS TO SYSTEMATIC REASONING
Lokendra Shastri
Dept. of Computer and Information Science
University of Pennsylvania
Human agents are capable of representing a large body of structured and
systematic knowledge and drawing a variety of inferences based on this
knowledge with extreme efficiency. These inferences are by no means
trivial and support a broad range of cognitive activity such as
understanding language and performing commonsense reasoning. We present
work on connectionist knowledge epresentation and reasoning systems that
is a step toward a computational account of this remarkable reasoning
ability. A key technical problem that must be solved in order to
represent and reason with structured knowledge is the dynamic creation
and propagation of bindings. We show how this problem may be solved
using simple phase-sensitive oscillatory neuronal elements that allow the
system to maintain and propagate a large number of dynamic bindings
simultaneously.
Poster presentations
ARCHITECTURES FOR UNCERTAIN REASONING
James Austin, Thomas Jackson, and Alan Wood
Dept. of Computer Science
University of York, England
This paper presents the basis of a neurocomputer architecture for
uncertain reasoning. The arguments for applying neural networks to this
domain are discussed together with the broad architecture of the system.
The work considers the boarder range aspects of a neural computer
architecture, not just the implementation of the neural network. It is
shown how a two stage system comprising of a front end SIMD processor and
a tightly coupled back end neural processor is to be used in initial
investigations. A neural associative architecture is considered for the
back end processor that is based upon the ADAM system, a high speed
extensible network developed at York. The major issues that relate to the
full development of the system are presented, along with an indication of
the applications of such a system.
- ------------------------------
SETS, TUPLES, GRAPHS, PROPOSTIONS, AND FINITE REGULAR LANGUAGES
IN A HOPFIELD-STYLE NETWORK
Arun Jagota
Dept. of Computer Science
State University of New York at Buffalo
A Hopfield-style one-layer network has been developed [1] whose stable
states are the maximal cliques in the underlying graph, allowing analysis
and proof of many of its properties using graph-theoretic techniques.
This network with graph-theoretic properties is also found to be superior
to any known one-layer network model as a content-addressable
memory/information storage device in it's space/time complexity and
storage capacity. In particular, it has exponential storage capacity,
although spurious memories do develop. As importantly, it's relationship
with graph theory, in particular the relationship between its stable
states and maximal cliques has established it as a maximal clique
machine. In other words, any existing or not-as-yet-discovered
reductions of problems to maximal cliques in graphs also implies that
such problems can be mapped onto the network. At this workshop, we will
describe five key problems that can be mapped onto the network, storage
and retrieval of sets (for CAM), storage and retrieval of Tuples (for CAM
and databases), storing arbitrary graphs, storing CNF Propositional
formula/testing for their truth-assignments and storing finite-regular
languages/testing for membership. The ability of the network on these
problems and their refinements and it's near-optimal space and time
efficiency may have important consequences for Knowledge Representation.
Moreover, the dynamic properties of the network, as proven in [1], are
quite different from those of other networks and may have important
consequences for knowledge retrieval from noisy or incomplete information
or information containing multiple hypotheses.
Storing Sets and Tuples is a direct consequence of the network Learning
rule [1]. The network has significantly better CAM properties for Tuples
than Sets [1]. Sets can be stored as Tuples by a suitable transformation.
Any graph can be stored in the network by presenting its edges as binary
sets. CNF Propositional formula and finite-regular languages are stored
by transforming their expressions to particular kinds of graphs and
storing these graphs in the network. These transformations will be
described at the workshop and it will be shown how such storage is
accomplished. For a restricted class of finite regular languages, the
network stores any language in this class perfectly and can test any
string for membership in one time step. There are languages in this
class that have an exponential number of strings. Even for such
languages, the network needs only polynomial storage (number of
connections) and can be trained in polynomial time. Finally, changing
the value of a global network parameter allows the stable states to be
subgraphs of a minimum density, where the two extremes of this global
parameter value give rise to stable states that are connected components
at one extreme and maximal cliques at the other[1]. This and other
graph-theoretic properties are proven in [1] and their implications for
clustering in general and Concept and Knowledge clustering in particular
will be discussed at the workshop.
And lastly, this network model has been implemented in software and
applied to moderate-size real-world problems. We will also use this
software on detailed examples of the problems mentioned above,
specifically to obtain results which seem theoretically difficult and
report such results at the workshop. We will also offer this software to
any interested researchers.
[1] A new Hopfield-style network for content-addressable memories, Arun
Jagota (1990), Technical report TR 90-02, Department Of Computer Science,
State University Of New York At Buffalo. USA
SELECTIVE ATTENTION IN CONTEXTUAL MEMORY
Nilendu Jani
Dept. of Computer Science
University of Texas at Arlington
The primary purpose of this paper is to provide a connectionist model for
the cognitive process of sleectively choosing a concept out of a pool of
candidate concepts present in the current context. The distinction here
is the need for contextual information.
Initially key issues concerning the modelling of memory are identified by
asking a few fundamental quesitons about blocking, interruption,
suspension, resumption, attentional span, spreading activations,
consciousness, recognition, retrieval, learning, retaining, and
forgetting. The significance of contextual memory and selective
attention in comprehensive modelling of human brain and day-to-day
cognitive processing is discussed. The recent trends in neural network
based architectures in modelling selective attention are identified. The
one particularly of interest in this paper is the model based on
competition by Grossberg and Levine, which uses drive representations for
selective attention. The Grossberg-Levine model is extended to take
context into account. The enhanced model concentrates more on the role
of selective attention in retrieval than in learning or conditioning.
Furthermore, the significance of separate attentions at the sub- system
level and at the global level is identified. The existence of contextual
memory, and the distinction of attentions at the sub- system and global
levels, are validated and explained on intuitive grounds, not on a
physiological or experimental psychological basis. A scenario depicting
a typical day-to-day event is given to illustrate the nature of selective
attention within contextual memory. Finally a mathematical connectionist
model of selective attention is proposed, which incorporates context and
different levels of attention.
NEURAL NETWORK FOR HIGH-SPEED IMPLEMENTATION OF RULE-BASED SYSTEMS
Arun Maskara
Dept. of Computer and Information Science
New Jersey Institute of Technology
Andrew Noetzel
Brooklyn Polytechnic University
We consider the use of a neural network for the parallel selection of
rules in a rule-based expert system. In our approach, the expert system
rules are presented as patterns that are learned by a neural network.
Each rule is learned after only one presentation. The learning mechanism
is similar to that of Grossberg's ART-1 model, but the architecture has
been adapted for the specific requirements of the expert system
application. After learning, the neural network acts as a
content-addressable memory. When presented with a partial rule
description, it produces a parallel specification of all rules that may
be applicable. The neural network is a multilayered, data-driven
network. After choosing the applicable rules for a particular problem
state, the architecture allows for the execution of multiple control
paths. Inferencing is implemented by allowing feedback between the
various levels of the network. A combinational logic net is designed to
act as the rule interpreter. The architecture of the neural network and
interpreter is demonstrated, along with examples. The results of
simulation examples are shown. Possible variations and generalizations of
the design are discussed.
USING AN ARTIFICIAL NEURAL SYSTEM TO DETERMINE THE KNOWLEDGE BASE
OF AN EXPERT SYSTEM
George Whitson, Cathy Wu, and Pam Taylor
Computer Science Department
University of Texas at Tyler
This paper gives a mapping of rule based expert systems into artificial
neural expert systems. While the mapping is not one-to- one, it does
show that the two systems are essentially equivalent. There are, of
course, many examples of artificial neural systems that are not expert
systems. We can use the reverse mapping of an artificial neural expert
system to a rule based expert system to determine the knowledge base of
the rule based expert system, i.e., to determine the exact nature of the
rules. This yields an automated procedure for determining the knowledge
base of an expert system that shows much promise. We have implemented
this expert system tool on several larger microcomputers, including an
Intel Sugarcube. The Sugarcube implementation is a very natural one.
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End of Neuron Digest [Volume 6 Issue 56]
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