Application of Cognitive Theories for Simulation of Robotic Models
Darush Davani, Frederick Ackers
Towson University
Towson, Maryland 21252
[email protected]; [email protected]
Keywords: Cognitive Robotics; Decision Field Theory;
Fuzzy Trace Theory; Robotic Intelligence;
Abstract
This study presents the reasoning and results
of simulating the use of cognitive psychology
theories as the foundation for robot intelligence.
From existing published theoretical work, a
cognitive-robotic-model developer has a plethora of
information at their disposal. This theoretical work
used for a robotic models may include statistics,
testing parameters, as well as various other
information, such as neural correlates which, may
allow for achieving robotic intelligence. We
investigate the utilization of Decision Field Theory
and its application to attention models. Furthermore
we examine the use of Fuzzy Trace Theory for goal
determination. The reasons for choosing these
selected theories as a basis of robotic intelligence
exploration shall be detailed. This paper summarizes
simulation model issues of using these selected
theories with existing progress and some new
directions for future work in this fast growing field.
1. INTRODUCTION
Utilizing cognitive psychology as a
foundation for cognitive robotics research allows the
robotics developer to obtain theoretical and practical
experimental results, methods, and possibly even
statistical information. Rather than programming a
robot in an ad-hoc methodology, using cognitive
theories for either their designated purpose or for new
purposes, stimulates the potential likelihood of
creating a truly cognitive robot. Psychology offers a
plethora of work on topics such as senses and sensory
information, memory formation, behavioural
patterns, or decision making. Some of the research
even offers neurological evidence and correlations as
a basis for the foundation of the research thereby
providing more information for analysis to aid the
robotic developer in implementation of the chosen
theory(s).
The beauty of using simulation models of
existing theories as a basis for robotic development
extends from the application of the theories to new
and perhaps unprecedented uses. These applications
of theories in an unprecedented manner come about
through the process of the robotic engineer
attempting to “understand” how the robot may
“think”, “act”, and “perceive” like a human (or
animal) does while at the same time attempting to
reconcile that theoretical understanding with
hardware and software issues presenting themselves
during the development process.
Studies of cognition may aid the robotics
engineer with ideas and foundational work for
implementation. At the same time, the robotics
engineer may aid in the study of cognition through
their implementation of selected works as well as
through the experimental results of applying theories
to new uses. Hence, cognitive psychology and
cognitive robotics studies may learn from each other
and make use of the work and time spent researching
by their cognitive counterparts. The psychological
studies detailing how things work in thinking beings
and the robotic studies detailing how such theories
could have application.
The utilization of the models of existing
cognitive theories and their application to new uses in
the robotic field, other than perhaps their intended
use as detailed in the theoretical papers, remains the
focus of this article. Two selected research works
shall have discussion for the remaining portion of the
paper. Decision Field Theory (DFT) and its
application to Robotic Attention will have some
discussion. In addition, a discussion will also take
place concerning the utilization of Fuzzy Trace
Theory (FTT) to decision making and determination
of goal completion. Ending the paper, a presentation
of future research shall have a brief mention.
2. BASICS OF DECISION FIELD THEORY
Decision Field Theory provides valuable
insight into decision making during uncertainty that a
person may use during the course of an ordinary and
daily experience [1], [2]. Within the theory of DFT
reside the combination of “approach-avoidance
ideas” for conflict, and the “information processing
theories of choice response time” [1]. These ideas of
conflict and response time create a fundamental
theory for prediction and the “distribution of choice
response times” [1]. DFT began with a basis of
approach-avoidance theory, and Subjective Expected
Utility (SEU), but expands them through the addition
of a continuous time aspect.
In [1], it is stated that for any given action,
there exists a subjective weight assigned to each
payoff with the SEU theory. Determination of the
SEU for an action with respect to a certain event
becomes achievable through the average of all the
weights.
Deterministic SEU represents, for any given
event, the amount of attention paid to a certain action
for that event. Equation 1 shows the calculation for
the valence v of the Deterministic SEU where u(y) is
the utility of payoff y and w(Sj) is the subjective
probability weight given to event Sj [1].
( )
( )
( )
(
)
(
)
Random SEU encompasses the idea of
Deterministic SEU and adds the idea that attention
may alter between decision making times. The
preference state P for the preferred object of attention
may be determined from Equation 2 [1].
Eq. (2)
Sequential SEU adds the fact that attention
may switch between various events within a single
decision making time. The preference state for the
preference object of attention after trial n is shown in
Equation 3 [1].
( )
( )]
[ ( )
( )
Random Walk SEU generalizes the
Sequential SEU by adding prior experience and an
anchor point for that experience. In Equation 4, z is
the anchor point for prior experience [1].
( )
( )]
( )
[ ( )
Linear System SEU adds a time variance to
the system, called the grow-decay parameter
displayed as s in Equation 5 [1].
( ) (
)
(
) [ ( )
( )]
( )
Approach-avoidance theory introduces the
idea of a function of commitment distance to an
action that alters the weight given to an action. This
commitment distance is function is shown in
Equation 6 where [
( ) is the new valence input
and c is the goal gradient parameter [1].
( ) [
(
)
(
) [
( )
( )
DFT formulates that a combination of time,
the various SEU as well as approach-avoidance
theory provides for a decision making theory in
which, as time approaches zero, the system takes on a
wave-like aspect of behaviour. Equation 7 shows the
calculation for the DFT where h is the time taken to
process one sample pair and t=n*h where n is the
number
of
processed
samples
[1].
( ) [
(
)
(
)
[
( )
( )
[2] extends the previous work from a binary choice
and details the DFT for a multi-choice situation.
[3] contains a description of a preference
state vector and coordinates that contain a preference
state for each of a series of actions. The state within
the preference state vector may contain an approach
state or an avoidance state as well as the magnitude
of the state. In [3] there exists a feedback matrix,
“self-feedback coefficients” and lateral inhibition
connections. The feedback matrix changes across
time. The “self-feedback coefficients” exist as
“inversely related to conceptual distance between
actions” [3]. A contrast matrix, value matrix, and
attention vector combine as a product to become the
input for the system. The contrast matrix foretells the
advantage of an action versus other actions. The
value matrix represents the “affective evaluation of
each possible action” [3]. The fluctuation of the
attention vector represents attention changes in an
uncertain state at a particular given time. Therefore,
decision reaching becomes determined by a
controlled stopping task. The controlled stopping task
may exist as something internal or external. The
internally controlled stopping task states that there
exists a threshold that must be reached by any
preference before a decision may be made. The
externally controlled stopping task states that the
preference with the maximum magnitude will be
chosen at the decision time.
3. REASONS FOR DECISION FIELD THEORY
Decision Field Theory selection resulted
from researching an idea pertaining to what
constitutes attention? The first question formulated
stated simply “What constitutes attention?” Upon
answering this question, a postulation was created
that said simply “Attention may relate to the focusing
of the mind”.
For this research of “focusing of the mind”,
a decision making algorithm was proposed in an
effort to decide what item from memory should
receive focus in an otherwise chaotic mind. A robotic
mind may be chaotic in the sense that there exists no
clear direction of cognitive thought. Therefore the
chaotic mind in this sense requires some direction
which it could potentially receive from a decision
making algorithm used to “decide” internally what
should be the object of attention. DFT was chosen as
such an algorithm due to its capability to make
decisions in the face of uncertainty detailed in [1],
[4].
4. DECISION FIELD THEORY RESEARCH
Application of DFT for the use of creating
attention in a cognitive robot proved a difficult task
to say the least. Much of parameter initialization
proved a trial and error sort of testing methodology
due to lack of available information. In addition,
selection of chosen items passed to the DFT
algorithm as choices showed that depending upon
selected criteria, a steady state of attention may or
may not result from the use of the algorithmic output.
[5] showed that sustained attention did result from
the use of DFT when but only when certain
conditions were met. In addition, the research showed
that only minute sustained attention became possible
using the selected parameter values and the certain
sets of features. Such attention to specific features of
interest resulted from passing State information, such
as hardware, battery life, etc. or from specific
information such as colors. Depending upon the
parameter values utilized, concentration upon solely
state information showed possible for somewhat
extended periods of time due to the increasing
likelihood of an item chosen for attention once, being
chosen again. Attention to specific colors proved
possible though only for short periods of time,
perhaps due to the colors quick decay rate in
memory, while the state information has a much
lower decay rate.
Decay rate of other types of memory
information may have affected the results from other
types of memory information. This may happen due
to the decay happening faster than the algorithm can
select the choice and update the decay rate to a lower
value. The decay rate did not receive extensive
testing during this research trial. Though, the effects
of decay rate upon “proper” DFT parameter values
for varying memory information types may result in a
valuable research experiment.
No matter what parameter values were
chosen as the values for the DFT, some memory
information types were not selected as a focus of
attention for any significant length of time. The cause
of this lack of focus for some memory types remains
unknown, though a possible suspect appears as
incorrect parameter values. While not a complete
success in achieving sustained attention for numerous
memory types, the research did show that a more
extensive look into the DFT use for attention may
prove useful, particularly concerning the use of
specific or generic parameters and how they relate to
differing memory information types.
The research into utilizing DFT brought a
conclusion of, “if we are paying attention to an item
in relation to completing a goal, how do we know
when we completed the goal? How do we assign
credit for the goal completion?” In an effort to solve
this goal completion question left over from the DFT
research, the Fuzzy Trace Theory research was
proposed to attempt to look into the matter of goal
completion.
5. BASICS OF FUZZY TRACE THEORY
Fuzzy Trace Theory (FTT) provides
interesting insight into the potential workings of the
human memory system and memory formulation [6],
[7]. Fuzzy-Trace Theory defines memory as
operating upon the concepts of verbatim and gist.
One could think of verbatim items of storage as facts,
details of events, etc. Gist traces would then have the
description of extractions of meaning from the facts
or events. One could also consider a gist trace a
relational, an elaboration, or a semantic property of
an item while the verbatim contains the form and
item specific information [6].
Within Fuzzy-Trace Theory, gist traces
appear at the encoding of stimuli signals in the brain,
while verbatim storage takes place during the
processing of the encoded signals [6]. Another way
of describing takes the form of gist creation
happening at the precognition level so that they act as
a cue for an item of verbatim which undergoes
creation at the post-cognition level. Gist traces
appear to have a longer accessibility time than do
their verbatim companions [7]. Verbatim memories
seem as though they have improved functioning with
regards to recognition tests. Gist traces, on the other
hand, seem as though they have improved
functioning with regards to meaning preserving tasks.
Verbatim traces seem to produce feelings of
remembering in a conscious manner [7]. Or to put it
another way, verbatim produces a feeling of
remembering due to its operation with explicit
memory types. A gist trace on the other hand,
produces a feeling of knowing, rather than
remembering, in a global manner due to its operation
with implicit memory types [7]. Gist traces may
sometimes produce vivid remembering experiences
due to repeated cue's increasing the strength of the
gist, retrieval cue's that specifically appear designed
to retrieve implicit memory types, or during tests
designed for responding based upon the meaning of
something.
Several principles exist for Fuzzy-Trace
Theory.
One such principle has to do with
relationships. Gist traces utilize relationships in both
a pairing and a global manner [6]. One may think of
an example of a pairing relationship as more yellow
jelly beans then purple jelly beans. For a global
relationship, think of cats have the highest count,
dogs the least count, or ferret's less than half. Gist
traces have the ability of use and generation in a
more rapid manner then does verbatim, this makes
gist traces a more economical phenomena than
verbatim memories, and increases the use of gist
traces for tasks such as problem solving [6].
Another principle concerns the availability
of memory and states that gist traces appear more
persistent then verbatim does over time, and therefore
memory retrieval utilizes gist traces primarily due to
their availability [6]. A reason for such availability
may take the form of generalization due to gist traces
having an increased range of situations in which they
have usefulness. Perhaps the usefulness of gist traces
also has to do with the simplicity of their processing
due to their generalization nature, making processing
such memories not as complicated for reasoning tasks
as processing a verbatim and would the same task.
Gist traces may also have use in the reconstructing
lost verbatim information, much in the same way as
the extraction of gist traces from verbatim may also
take place [7].
Finally, gist traces, like verbatim, have the
property of easiness of storage [6]. Gist traces may
undergo creation at the time of encoding memories
and a reduction and abstraction take place from
verbatim to gist. With the generalization
characteristic of gist traces, a possibility exists for a
hierarchy of gist traces in the brain. Both gist traces
and verbatim may have a place of temporary storage
in the working memory [7]. Working memory
contains an increased count of verbatim then gist,
though gist becomes stored in relatively longer term
storage, producing the characteristic of stronger
activation for a gist trace then its verbatim
counterpart.
A spectrum from vague patterns (gist traces)
to specific details (verbatim) exists in which the gist
traces have preference due to their nature of
availability, the lower amount of effort required for
processing comparisons, the lower amount of
complexity of information for processing, as well as
the ability of processing gist traces in parallel [7].
Gist traces have locations of storage in working
memory as previously mentioned but also in ShortTerm as well as Long-Term Memory facilities
increasing the preference of gist traces over their
verbatim counterparts.
6. REASONS FOR FUZZY TRACE THEORY
Fuzzy Trace Theory selection resulted from
a question of DFT research in which the question
arose as to rather how one should solve the credit
problem for determining goal or task completion. The
credit problem in association with goal determination
basically states “How do we determine completion of
a goal?” From a robotic standpoint, determination of
goal completion proves a somewhat difficult task due
to a lack of cognitive insight into what constitutes
“completion”. The robot merely understands
numerical concepts or items that are capable of
translation to numerical quantities. Therefore, when
attempting to translate conceptual commands into
numerical results for decisions, the conversion
process of concept to quantity and vice versa proves
an interesting yet vitally important task.
Selection of FTT came about due to its
inherent reliance upon conceptual information for
information processing. Therefore, a proposed idea
for translation between conceptual information and
numerical quantities the robot may understand came
in the form of fuzzy operators. Fuzzy operators when
combined with fuzzy concepts in memory might
provide the solution for determining goal completion.
At the same time, operation upon fuzzy concepts in
memory instead of quantifiable factual data might
allow for easier incorporation of conceptual gist
related information into the system without requiring
hard-coding of factual basics. This would provide the
system with a method of expansion, or at least a
somewhat easier method of expansion than otherwise
provided, allowing relatively dynamic incorporation
of new information into an otherwise somewhat static
system.
7. FUZZY TRACE THEORY RESEARCH
Implementation of Fuzzy Trace Theory
remains an on-going task at the time of this writing.
However, much work remains completed toward
achieving the goal of utilizing FTT with a
combination of fuzzy logic for goal determination.
One of the fundamental questions that arose while
performing this research states “How does one create
gist within a robotic memory?” To answer this
question, first a gist concept needed creation. This
gist concept, as it relates to robotic memory, would
evolve over time. Gist representation began as
mimicking a fact with different names for the
parameters. As time progressed, gist representation
moved more toward attribute translation from fact
representation to word representations for attributes.
In addition, due to part of FTT stating that a
gist may recreate a fact within memory, a decision
became made to store a reference to the fact within
the gist representation. This storing of a fact within a
gist allows for the recreation of the fact at a later time
due to the inherent decay placed upon facts within the
system. A method of gist creation from a fact also
exists within the system. Thus the rule stating that a
gist or a fact may be recreated at a later time, if
missing, has been fulfilled.
A partial evaluator methodology already
existed within the system. This partial evaluation
methodology then became expanded to encompass a
fuzzy logic system that translates keywords from the
gist representations into numerical values usable by a
decision making algorithm. The fuzzy logic
implementation started within several routines. Each
routine encompassing comparisons of various like
terms and formulating numerical codes to determine
which term remains greater or less than another term.
This gist and fuzzy logic combination will allow for
the use of terminology in robotic goals that then may
be translated into numerical concepts that the robot
may understand. See Figure 1 for an example of the
difference in a robotic mind between the
representations of how the Corner (verbatim) looks
versus how the CornerGist (gist) looks in memory.
Figure 2 displays the two-dimensional simulator for
watching the robot in action as it was run by custom
software that created the verbatim and gist in the
robots memory as based upon the input from the
MobileSim simulator. MobileSim is a simulator for
ARIA based robots produced by the company Adept
MobileRobots [8].
in gists being stored for very long periods of time
before decay thus acting as a sort of long term
memory. Decisions of when to execute a given
command within the system were changed from their
original implementation using verbatim only to a new
implementation using verbatim when possible and
gist if the verbatim does not exist. This, while an
alteration from what would be an exact
implementation of FTT, exists as a necessary
implementation due to the commands currently
operating based upon verbatim information thus
redesigning the entire system to utilize gists for
command execution will take much more effort than
current time has allowed to date.
An example of a use of the system in its
final implementation may be thought of as, if one
were to tell the robot to go to face northwest. As a
basis, the robot only knows the numerical values in
degrees for which it is facing. For this example, let us
say the robot is facing 270 degrees. The robot in a
natural implementation would have no concept of
“northwest”. However, utilizing facts, gist
representations, and fuzzy logic, with translation
mechanisms in place between them, the robot would
then “understand” the current direction it faces,
acknowledge that the desired direction does not equal
the current one, and then move until the desired
direction equals the current direction. When finished,
this research may provide an insight into the use of
gist memory within a robotic environment, as well as
providing insight into how one may make a decision
using gist representations.
Figure 1. Corner as Gist (top 4) and Corner as
Verbatim (bottom) sample display
As work on the system has progressed, gist
traces are given a much lower decay rate in memory
then the verbatim items allowing for more long term
storage of gists. Repeated retrieval of a gist increases
its activation which in effect increases the length of
time that the item will be retained in memory. This
activation combined with a lower decay rate results
Figure 2. MobileSim platform used for research
8. CONCLUSIONS AND FUTURE RESEARCH
Simulation of cognitive models in robotics
may not be the easiest task, though it rewards us with
valuable information such as what seems possible,
and what does not work. The Decision Field Theory
experiment showed that utilizing a decision making
algorithm for sustained focus of attention may prove
useful. Fuzzy Trace Theory utilization for goal
determination has proved trickier due to attempting to
figure out how best to represent the gist within the
simulation. As Fuzzy Trace Theory is still a work in
progress, more results will be known in the future as
the work continues.
Future research for DFT related items
include determination of optimal parameters for the
algorithmic implementation, looking at whether DFT
would be preferential at the sensory or cognitive
levels of attention, and determining whether DFT
would better suit decision making in addition to
utilization for attention or shall DFT use restrict itself
to decision making or attention but not both. The
effects of decay rate when utilizing efficient DFT
parameter values for various types of memory
information appears as a possible research
experiment which may prove useful. In addition, a
look at whether or not requirements for utilization of
specific or generic parameter values exists depending
upon the contents of the memory information
selected as a candidate for focus of attention may
prove useful once more information becomes
available about the DFT parameters and their use in
attention research.
Future research for FTT includes answering
the question of how to translate sensory data into
conceptual gist data for a robotic system, an in depth
study of robotic decision making based upon gist
primarily, and study of algorithmic translation
between gist and numerical output required for
robotic execution.
ACKNOWLEDGEMENTS
We would like to acknowledge ARL at
Aberdeen Proving Grounds, MD for their support.
We would also like to thank Troy Kelley and Eric
Avery for their continued discussions involving these
projects.
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BIOGRAPHY
Dr. Darush Davani has several years
teaching undergraduate and graduate courses on
robotics and simulation. He has been doing research
work in the robotics and simulation field. He has
worked in simulation and previously done research
on software reliability work with NASA. Dr. Davani
has published several articles related to robotics over
the years. He has received several ARL grants over
the years for work on robotics. He has also received
NSF grants for work on simulation.
Frederick Ackers is a Doctoral Candidate in
Information Technology. His focus is on cognitive
robotics. He did his master’s thesis on attention in
cognitive robotics. He has worked as a research
assistant for the past 2 years under the guidance of
Dr. Darush Davani.