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An Application of Context-Learning in a Goal-Seeking Neural Network.


Abstract and Figures

An important function of many organisms is the ability to use contextual information in order to increase the probability of achieving goals. For example, a street address has a particular meaning only in the context of the city it is in. In this paper, predisposing conditions that influence future outcomes are learned by a goal-seeking neural network called Mona. A maze problem is used as a context-learning exercise. At the beginning of the maze, an initial door choice forms a context that must be remembered until the end of the maze, where the same door must be chosen again in order to reach a goal. Mona must learn these door associations and the intervening path through the maze. Movement is accomplished by expressing responses to the environment. The goal-seeking effectiveness of the neural network in a variety of maze complexities is measured.
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An Application of Context-Learning in a Goal-Seeking Neural Network
Thomas E. Portegys,
Illinois State University
Normal, Illinois 61790
An important function of many organisms is the ability to
use contextual information in order to increase the
probability of achieving goals. For example, a street
address has a particular meaning only in the context of
the city it is in. In this paper, predisposing conditions that
influence future outcomes are learned by a goal-seeking
neural network called Mona. A maze problem is used as
a context-learning exercise. At the beginning of the
maze, an initial door choice forms a context that must be
remembered until the end of the maze, where the same
door must be chosen again in order to reach a goal. Mona
must learn these door associations and the intervening
path through the maze. Movement is accomplished by
expressing responses to the environment. The goal-
seeking effectiveness of the neural network in a variety
of maze complexities is measured.
Connectionism, context-learning, goal-seeking, neural
1. Introduction
Context learning is an important function for many
organisms, especially humans. Behavior that is socially
accepted at a sporting event will not be welcome in a
classroom setting. My cats consider their chances of
getting fed far better after I arrive home. These are
examples of the significance of context: utilizing
knowledge of one’s overarching situation and possibly
making certain prior arrangements in it can have a great
deal to do with a prospective outcome.
There are many references to general context learning
in the literature [1,2,3]. Various specialized approaches
also exist. For example, context learning has been
described as hierarchical sequence learning by Sun and
Giles [4]. Researchers have also proposed context models
of brain and behavior such as Howard and Kahana's
Temporal Context Model (TCM) of the recency and
continguity memory effects [5], and Hasselmo and
McClellands' model of the hippocampus' role in memory
formation [6]. In the robotics field, Brooks and Maes
trained a robot operated by a hierarchy of control
contexts to walk by using environmental feedback [7].
For non-symbolic learning, mathematical methods have
been developed to optimize reinforcement produced by
an environmenal context function [8].
The subject of context learning narrows considerably
as an application of artificial neural networks. Perhaps
some of the most related work is in the field of grammar
learning [9,10] and text classification [11] using
recurrent and cascading neural networks. In the grammar
learning studies, neural networks are trained to recognize
sequences of inputs produced by a grammar, and are later
tested on their predictive performance given incomplete
sequences. The neural network plays a passive
recognition role in these experiments. In our study, the
aim is to allow the neural network to take an active part
in the learning process by producing responses that affect
state-transition probabilities. The predisposing conditions
that affect future outcomes are environmental contexts.
Learning these contexts allows the neural network to
navigate its environment to reach a goal state.
The purpose of this project is to develop and test a
learning mechanism suitable for a goal-seeking neural
network called Mona. Although a connectionist
architecture, Mona is more of a state-based planning
system that a conventional pattern classifying neural
network. Planners [12] are typically symbolic, not
connectionistic systems, necessitating a novel learning
solution for Mona.
Mona has modeled complex behavior on a number of
tasks, including foraging and cooperative nest-building
[13,14]. For an exhibit of the nest-building task, see
/NestViewer.html Mona features an integrated
motivation mechanism designed to produce responses
that yield need-reducing outcomes.
For this project, a maze problem is used as a context-
learning exercise. At the beginning of the maze, an initial
door choice forms a context that must be remembered
until the end of the maze, where the same door must be
chosen again in order to reach a goal. Mona must learn
these door associations and the intervening path through
the mazes.
One way that animals can be taught is by a
conditioning process known as behavior shaping [15].
Mona can be taught by conditioning as well, although the
response-overriding technique used in this project affords
an accelerated learning option.
1.1 A Review of Mona
This section describes the existing system that will
incorporate the new learning capability. Mona is based
on the rationale that brains are goal-seeking neural
networks. It has a simple interface with the environment,
shown in Figure 1. All knowledge of the state of the
environment is absorbed through senses. Responses are
expressed to the environment with the goal of eliciting
sensory inputs which are internally associated with the
reduction of needs.
Figure 1 – Mona/Environment Interface
Events can be drawn from sensors, responses, or the
states of component neurons, calling for three types of
neurons. Neurons attuned to sensors are receptors, those
associated with responses are motors, and those
mediating other neurons are mediators. Mediators can be
structured in hierarchies representing environmental
contexts. A mediator neuron controls the transmission of
need through and the enablement of its component
To elucidate by example, consider this somewhat
whimsical task: let Mona be a mouse that has been out
foraging in a house and now wishes to return back to her
mouse-hole in a certain room. For the sake of keeping
peace with her fellow mice, she must not make the
mistake of going into a hole in another room. Figure 2
shows her neural network at this juncture.
The triangle-shaped object at the bottom is the receptor
neuron that fires once she has reached her hole; the
inverted triangles are motor neurons that accomplish the
responses of going to the correct room (Go Room), and
going into the hole (Go Hole). The ellipses are mediator
neurons. Each is linked up to a cause and effect event
neuron. The “Hole Ready” mediator is not enabled,
reflecting the importance of not going into a hole in the
wrong room. The “Room Ready” mediator is enabled,
signifying an expectation that if its cause event fires, its
effect will also fire.
The “Home!” receptor neuron has a high goal value,
indicating that it is associated with a need. Because of
this, motive influence propagates into the network,
flowing into motor neurons whose firings will navigate
to the goal. Since the “Hole Ready” neuron is not
enabled, the motive bypasses the “Go Hole” motor
neuron in search of a mediator whose firing will enable
“Go Hole”. Since “Hole Ready” is an effect of “Room
Ready”, it flows into the “Go Room” motor via the
enabled “Room Ready” mediator and causes it to fire
(double outline).
Figure 2 – Intitial Mouse Network
Figure 3 – Final Mouse Network
The flow of motive illustrates how mediators
representing contexts work together. The appropriate
context for “Hole Ready” is “Room Ready”, which
means that the latter should necessarily contribute
something to the former in order to enable it. This
something is called a wager. A wager temporarily
modifies the enablement of a mediator that is the effect
event of another mediator. It is called a wager because
the base-level enablement of the wagering mediator will
be evaluated based on subsequent firing of the effect
In Figure 3 the “Go Room” cause firing can be
understood as a conditional probability event: given that
Mona is in the correct room (“Room Ready”), she is
quite certain that she can go into her own hole. This
accomplished by a wager from “Room Ready”, triggered
by “Go Room”, that boosts the enablement of “Hole
Ready”. After this enablement occurs, motive flows into
the “Go Hole” motor neuron, causing it to fire.
Subsequently the Mona senses that she is home in her
2. Maze Environment and Training
For this project mazes are generated that embody a
context-learning problem. An example maze is shown in
Figure 4.
Figure 4 – An Example Maze
The Start room has a variable number of doors. Only one
of these doors actually leads to room A. The middle
portion of the maze consists of a randomly generated
path of a variable number of rooms connected by doors.
All the M rooms in the path appear to Mona exactly the
same; however, only one door can be opened to reach the
next room. At room B at the end of the path, the learner
must choose the same door as that leading from the Start
room to A in order to reach the Goal room. It can be seen
that the context in this problem is to retain information
about which door led from the Start room in order to
repeat this choice at room B. In order to ensure that the
context information varies, for each trial the door leading
from the Start room is randomly determined. Since the
learner has no a priori knowledge of which door this is, it
is allowed several tries to determine the successful one.
However, once past the Start room, any subsequent
wrong door choice counts as a trial failure.
Mona was trained on each maze in two phases. For the
first phase of 150 trials, its memory was allowed to retain
150 mediator neurons. Mona’s behavior in the first phase
was shaped by overriding its responses with the correct
ones. The door-choice correspondences between the Start
room and room B were established by exposing Mona to
various door configurations in a maze with no middle
path (M rooms). The middle path from room A to B was
taught by directing Mona along this path. A schedule of
mixing end-maze and middle-maze learning proved
effective. In the next training phase, Mona was allowed
to run free for 100 trials to allow it to adjust the
enablements of mediators to more accurately reflect maze
probabilities. For example, if the Start room featured 3
possible doors, each door would be successful 33% of
the time. It was also forced to cull its memory down to a
final limit of 100 mediators in this phase. An example of
a training run over a full maze is given in Appendix A.
3. Learning
A further refinement in the definition of a mediator is
required here. A mediator consists of a cause event, an
effect event, and a variable number of intermediate
events representing a specific temporal sequence between
cause and the effect. For mediators overseeing receptor
and motor events, the cause and effect must be receptors,
and the intermediate event a motor, thus embodying a
stimulus-response-stimulus sequence. However,
additional intermediate events are possible as long as
they conform to an alternating receptor-motor pattern.
The generation of new mediators is conceptually
straightforward. A history of firing neurons is retained
that serves as a basis for hypothesizing new cause and
effect relationship that are incarnated as new mediators.
How far back in time the history is kept is a system
parameter. Furthermore this can vary based on the
“level” of mediators, allowing higher level mediators to
associate events more distantly separated in time. For the
maze problem, the history was set to allow the highest
possible mediators to oversee events spanning the entire
maze, thus allowing the correct door choice to be
Figure 5 – “Bridge Mediator” at Start Room
Another essential learning activity besides the
generation of new mediators is the evaluation of existing
ones. This allows more reliable (enabled) mediators to be
retained in memory. The “base” enablement of a
mediator is updated as follows:
base-enablement = wagersuccessful / wagers
This means that the base-enablement of a mediator is
roughtly equivalent to the average number of successsful
ones. However, since each wager can have a variable
weight, this is a weighted average.
Figure 5 shows three learned mediators that oversee
the association of taking door 0 at the beginning and end
of the maze. This snapshot was taken while Mona was in
the Start room, indicated by the firing receptor.
The “Bridge Mediator” mediates two otherwise
unrelated mediators: “Start Mediator” and “Goal
Mediator”, each of which oversee taking Door 0 from the
Start and B rooms, respectively. As can be seen, neither
of these mediators are completely enabled, reflecting the
maze’s inherent indeterminacy. Another item of note is
the motive value of 1.51 at the “Open Door 0” motor, a
value propagated from the “Goal Room” receptor. Of
course, at this junction the other doors that are not shown
also receive equivalent motivation.
Figure 6 – “Bridge Mediator” at Room A
Figure 6 shows the same set of mediators after a
successful attempt to reach room A via door 0, indicated
by the firing of the “Open Door 0” motor and the “Room
A” receptor neurons. Note that since the “Start Mediator”
has also fired, being the cause event of “Bridge
Mediator”, a wager has boosted the “Goal Mediator”
enablement to 100%. In effect, since conditional event of
using door 0 worked at the beginning of the maze, it is
now a certainty that door 0 will succeed at the end. Note
that the state in Figure 6 will persist while the middle
maze navigation proceeds independently, serving as a
background context whose influence asserts itself later in
4. Results
Testing consisted of varying the number of doors
appearing in each room and the length of the path in the
maze, and measuring the success rate of the learner. Each
data point represents the average performance of 10
randomly generated mazes (10 seemed sufficient as there
was little variation in results between mazes). The results
appear in Figure 7.
The general observation is that the number of doors per
room has a significant effect on the success rate. This
was not unexpected, since with more choices presented
in each room, there are more learned mediators that
possibly apply. Most of the errors in these cases arose
from the influence of mediators being applied out of
context. Many of these mediators were deemed
“parasites” because they grow stronger under the
guidance of more reliable mediators, until at last able to
exert a wrong choice. This diminishes them for a time
until a new growth cycle begins.
Generalized pruning techniques proved mostly
effective against parasites. However, as a general
philosophy, some of them can be thought of as
representing possibilities of environmental variation, and
as such should not be utterly exterminated. In sum,
learning new mediators was quite easy, unlearning poor
ones proved difficult.
Path Length
Success Rate
Figure 7 – Maze-learning performance
As an additional observation, the length of the middle
maze proved to have little effect on the success rate. As it
turned out, a single mediator could be reliably learned to
impeccably navigate this portion of the maze.
5. Discussion
One way of looking at the method presented here is as an
extension of reinforcement learning [16] to context-
related problems. The purpose of reinforcement learning
is to learn paths through a state space to goal states.
Actions causing state transitions are scored with a utility
value according to how well they contribute to goal-
seeking. In this sense the utility of a mediator
corresponds to its enablement. In prototypical
reinforcement learning models, exemplified by Q-
Learning [17] and Temporal Difference Learning [18],
the state space is a flat Markovian space, necessitating
the embedding of context information into state labels,
which in turn can result in a proliferation of states. The
use of hierarchies is a powerful means of avoiding this
proliferation: they provide modularity and reusability.
For example, consider a state transition S0 S1 may
exist within context C0 and C1 wherein the two contexts
affect the transition probability differently. In a flat
space, S0&C0 S1&C0 and S0&C1 S1&C1 are
needed to express this. Moreover, context hierarchies
allow the dynamic linking of cause and effect chains that
are not explicity encoded. For example, suppose context
C0 has S0 S1, and in C1 has S1 Goal. The two
contexts can be linked through the shared state S1 to
create a goal path. If S1 were encoded in a flat space as
S1&C0 and S1&C1 the linkage information would be
As a verification, Q-Learning applied to the maze
problem results in the plot shown in Figure 8. As
expected, the lack of context information is reflected in
the random responses made at the end of the maze. For
example, if there are three possible doors the correct one
will be chosen one third of the time. It should also be
noted that the middle (M) rooms were uniquely marked
for Q-Learning, otherwise the performance would have
been much worse.
Path Length
Success Rate
Figure 8 – Q-Learning Maze Performance
The question arises as to how Mona differs from more
conventional, e.g. feedforward, artificial neural networks.
On an architectural level, Mona also creates new neurons
to represent learned relationships, rather than exclusively
modifying existing connection weights. As previously
noted, the destruction of ineffective neurons is also a
necessary function. This raises interesting parallels with
the development of animal and human brains which
exhibit growth and consolidation phases.
It may be surmised that recurrent networks could be
trained to recognize input patterns representing the
existence of contexts and to associate these with response
sequences. However, the use of recurrent networks to
retain temporally distant information is a significant
challenge for these networks. As an verification of this as
applied to the maze problem, a set of maze trials
involving Elman and auto-associative recurrent neural
networks built with the Stuttgart Neural Network
Simulator (SNNS) [19] proved disappointing. Neither
were able to learn more than short two-door mazes.
The most important functional distinction from
conventional neural networks is that Mona is a goal-
seeker, more like a planner than a pattern classifier. For a
goal-seeker, many state path variations may suffice to
achieve success. Pattern classifiers, such as feedforward
neural networks, can be used to recognize environmental
states. In sum, goal-seeking and pattern classification are
complementary techniques.
6. Conclusion and Future Work
The described technique allows Mona to successfully
learn the given context-related maze task. This represents
a significant accomplishment for a connectionist system.
Conventional artificial neural networks have primarily
focused on pattern classification tasks, yet this is only
one of the functions of a brain. The ability to seek out
goals in an environment in order to satisfy needs is also
of fundamental importance. Furthermore, these two
functions may not be as disparate as they seem; nature
has apparently built them out of the same components,
organized in similar fashions.
For future work, the representation of logical and
causal conjunctions is scheduled for testing. As an
example of a conjunction, a door might not open unless a
code is entered and a key is used. Therefore a logical and
relationship exists between the two causal events. Some
other tasks involve the necessity to learn inhibiting
influences, an essential aspect of many animal and
human behaviors.
The C++ source code and other reference materials are
available at:
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Appendix A – Sample Training Trial
Maze Dump:
Valid door = 2, Room id=0 mark=0 doors: 1 1 1 goals: 0
Valid door = 1, Room id=1 mark=1 doors: 1 1 1 goals: 0
Valid door = 0, Room id=2 mark=2 doors: 1 1 1 goals: 0
Valid door = 0, Room id=3 mark=2 doors: 1 1 1 goals: 0
Valid door = 1, Room id=4 mark=2 doors: 1 1 1 goals: 0
Valid door = 2, Room id=5 mark=2 doors: 1 1 1 goals: 0
Valid door = 2, Room id=6 mark=2 doors: 1 1 1 goals: 0
Valid door = 2, Room id=7 mark=3 doors: 1 1 1 goals: 0
Valid door = -1, Room id=8 mark=4 doors: 0 0 0 goals: 1
Room id=0 mark=0 doors: 1 1 1 goals: 0
Response: Door 2
Room id=1 mark=1 doors: 1 1 1 goals: 0
Response: Door 1
Room id=2 mark=2 doors: 1 1 1 goals: 0
Response: Door 0
Room id=3 mark=2 doors: 1 1 1 goals: 0
Response: Door 0
Room id=4 mark=2 doors: 1 1 1 goals: 0
Response: Door 1
Room id=5 mark=2 doors: 1 1 1 goals: 0
Response: Door 2
Room id=6 mark=2 doors: 1 1 1 goals: 0
Response: Door 2
Room id=7 mark=3 doors: 1 1 1 goals: 0
Response: Door 2
Room id=8 mark=4 doors: 0 0 0 goals: 1
Response: Wait
... So it seems reasonable that simulating simple creatures should involve the use of instinctive behaviors. The importance of this was impressed on me after training a neural network to solve a maze (Portegys, 2005). Without a teacher, there would be little hope of success. ...
... NestViewer.html). More recently (Portegys, 2005), it has learned mazes requiring retention of context information ( A brief review of Mona follows. ...
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