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Learning Environmental Contexts in a Goal-Seeking Neural Network


Abstract and Figures

An important function of many organisms is the ability to learn contextual information in order to increase the probability of achieving goals. For example, a cat may watch a particular mouse hole where she has experienced success in catching mice in preference to other similar holes. Or a person will improve his chances of getting back into his house by taking his keys with him. In this paper, predisposing conditions that affect future outcomes are referred to as environmental contexts. These conditional probabilities are learned by a goal-seeking neural network. Environmental contexts of varying complexities are generated that contain conditional state-transition probabilities such that the probability of some transitions is affected by the completion of others. The neural network is capable of expressing responses that allow it to navigate the environment in order to reach a goal. The goal-seeking effectiveness of the neural network in a variety of environmental complexities is measured.
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Learning Environmental Contexts in a Goal-Seeking Neural
Thomas E. Portegys
School of Information Technology
Illinois State University
Campus Box 5150, Normal, Illinois 61790, USA
An important function of many organisms is the ability to learn contextual information in order
to increase the probability of achieving goals. For example, a cat may watch a particular mouse
hole where she has experienced success in catching mice in preference to other similar holes. Or
a person will improve his chances of getting back into his house by taking his keys with him. In
this paper, predisposing conditions that affect future outcomes are referred to as environmental
contexts. These conditional probabilities are learned by a goal-seeking neural network.
Environmental contexts of varying complexities are generated that contain conditional state-
transition probabilities such that the probability of some transitions is affected by the completion
of others. The neural network is capable of expressing responses that allow it to navigate the
environment in order to reach a goal. The goal-seeking effectiveness of the neural network in a
variety of environmental complexities is measured.
Key Words:
Connectionism, context learning, goal-seeking, neural networks.
Context learning is an important function for many organisms, especially for humans. Behavior
that is socially accepted at a sporting event will not be welcome in a classroom setting. The odds
of obtaining a cookie from my Grandmother’s cookie jar may far exceed those of finding one in
my cookie jar, despite that we have identical jars. These are examples of the significance of
context: making certain arrangements in one’s environmental state prior to attempting an act can
have a great deal to do with the outcome. Many animals are capable of learning context from
their environment, as well as being taught by a conditioning process known as behavior shaping
(Carpenter 1974).
There are many references to general context learning in the literature (Bonzon, P. 1997;
Schank and Childers, 1984; Turner, 1998). Various specialized approaches also exist. For
example, context learning has been described as hierarchical sequence learning by Sun and Giles
(2001). 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
(2002), and Hasselmo and McClellands' model of the hippocampus' role in memory formation
(1999). In the robotics field, Brooks and Maes trained a robot operated by a hierarchy of control
contexts to walk by using environmental feedback (1990). For non-symbolic learning,
mathematical methods have been developed to optimize reinforcement produced by an
environmenal context function (Sabes and Jordan, 1996).
The subject of context learning narrows considerably as an application of artificial neural
networks. Perhaps some of the most relevant work is in the field of grammar learning (Bodén
and Wiles, 2002; Steijvers and Grunwald, 1996) and text classification (Wermter, Arevian, and
Panchev, 1999) 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 referred to as
environmental contexts. Learning these contexts allows the neural network to navigate its
environment to reach a goal state. The goal-seeking effectiveness of the neural network in a
variety of environmental complexities is measured.
The purpose of this project is to develop and test a learning method 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 (e.g.
Benson and Nilsson, 1995) 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 (Portegys, 1999 & 2001). See for an exhibit of the
nest-building task. Mona features an integrated motivation mechanism designed to produce
responses that yield need-reducing outcomes. It is currently being taught to learn
probabilistically generated mazes. This study represents a milestone in a continuing program of
development for Mona.
1.1 A Review of Mona
Mona is a model 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 neurons.
To elucidate by example, consider this task: Mona must get into her home from somewhere
out in the world, a locked door barring the way inside, thus necessitating the use of a key to
unlock the door. She needs to know several things, such as how to get to the door, how to unlock
the door, and how to enter her home through the unlocked door. Mona must produce a sequence
of responses to proceed from an initial keyless condition in the world to her home.
Figure 2 depicts the portion of Mona’s neural network which manages the entering of home
through an unlocked door. Let the house-shaped objects be receptor neurons, such as the one
marked “Door”; the inverted houses be motor neurons, such as “Move”; and the diamonds be
mediator neurons, such as “Enter home”. The numbers in parentheses indicate need levels, which
will be discussed later; suffice it to say for now that the “Home” receptor has been associated
with the reduction of a need, and is thus a goal for Mona. The numbered arrows proceeding from
a mediator indicate a sequence of neurons mediated by it. In this case, “Enter home” mediates a
sequence of events associated with the receptor “Door”, the motor “Move”, and the receptor
“Home”. This mediator thus governs the process of entering home by moving through a door.
The type of mediation exerted by “Enter home” is an enabling one, meaning that it allows firing
events to propagate enabling influences.
Initially the door is locked, thus the “Enter home” mediator is disabled, meaning that it cannot
function until preconditions establish an enabling context for it. This is represented by the dotted
outline of the mediator. In order to enable “Enter home”, another mediator must come into play:
“Enable enter home”. This mediator will enable the “Enter home” neuron when the “Unlock
door” neuron fires.
Figure 2 – Enable enter home/Enter home Mediators
However, the “Unlock door” neuron is also in a disabled state, requiring “Get key”, shown in
Figure 3, to fire as a precondition: the door cannot be unlocked without the key.
Figure 3 – Enable unlock door/Enable door
The final two pieces are supplied in Figure 4: how to get a key (“Get key”), and how to get to the
door from the world (“Go to door”).
Figure 4 – Get key/Go to door
Since these diagrams show the initial state of network, the “World” and “No key” receptors are
firing, denoted by the double outlines on their graphical symbols.
Recognizing environmental context is only part of the task addressed by Mona; needs
emanating from goals require a control mechanism to transform them into appropriate responses.
The networks that follow contrain mediators to manage two neurons: a “cause” and an “effect”.
For example, Figure 5 shows a situation in which Mediator2 must become enabled in order to
achieve the goal. Need in this case will flow from the Receptor goal into Motor1, Mediator1’s
cause, firing its associated response. This results in Mediator2 becoming enabled, as shown in
Figure 6. Need then flows into Motor2, firing its response, resulting in the subsequent firing of
the goal Receptor.
Figure 5 – Enabling Mediator2 Figure 6 – Mediator2 enabled
2.1 Environment
The purpose of the environment for this project is to allow the proposed learning techinque to be
verified. An environment is randomly generated given a complexity parameter defining the
number of contexts it contains. A context defines the probability of producing an effect given a
cause. Causes and effects are recursively defined as either stimuli to the learner or other
<context> ::= <cause>< probability><effect>
<cause> :: = <context> | <stimulus>
<effect> ::= <context> | <stimulus>
A network of contexts is generated as follows: an initial goal stimulus is created and directed to
produce the desired number of contexts surrounding it. This number is referred to as a branching
value. A stimulus may create a context by (1) changing into a context, spawning a pair of
cause/effect stimuli in the process, and/or (2) creating one or more parent contexts for which the
current node is the effect. The branching value of each created node is determined by randomly
dividing the current node’s branching value less the branching that it has generated itself. As
each context is generated, a random probability ranging from 1.0 to -1.0 is assigned to it. A
negative probability is an artificial quantity used in the accumulation process described below.
There is one restriction to force the neural network to learn individual cause and effect
probabilities: a cause stimulus can only branch by changing into a context.
Figure 7 shows a simple network of three contexts, denoted by the diamond shapes. A unique
number and associated probability is also shown for each. Stimulus 0 is the goal. This network
was generated by first branching Context 4 with a cause that subsequently changed into Context
1. Context 4 also branched as the effect for Context 6.
Figure 7 – Context Network
A stimulus can be in a “ready” or “fire” state, depending on whether it has been presented to the
learner (fire) or not (ready). A context has three states: “ready”, “set”, and “fire”. Initially ready,
a context transitions to the set state when its cause fires. When its effect subsequently fires, the
context fires. Probabilities accumulate as follows. When a context enters the set state, its
probability propagates, accumulating on its effect node. If its effect is also a context in the set
state, the accumulation continues. Thus in Figure 7, firing Stimulus 5 would have the effect of
moving Context 6 into the set state, which in turn would cause its -.35 probability to accumulate
at Context 4, making its probability .56. In order to set Context 4, Context 1 must fire, which
turn requires that Stimulus 3 and 2 must fire. The rationale behind the accumulating probabilities
is to provide a means for contexts to affect the outcome of responses by the learner. A negative
or zero probability results in no chance of a successful achievement of an effect, while a one or
greater probability results in a certain chance.
It is possible to generate environments that are complex and have improbable goal states.
Figure 8 is an example of an environment with eight contexts that contains an unlikely path to
Stimulus 0. The path is: Context 2 (.19), Context 9 (to increase Context 1 to .095), and Context
14 (.465). The combined probability is .005.
Figure 8 – An Improbable Network
2.2 Neural Network
After the context environment is generated, a corresponding neural network is manually created
that mirrors the cause and effect relationships in the environment. Mediator initial, or “base”,
enablements are set to a minimum value of .0001. The objective of the learner in this study is to
learn the probabilities of environmental contexts; generating new mediators from learned cause
and effect is a topic currrently under investigation.
Figure 9 shows the neural network corresponding to the environment depicted in Figure 7 after
learning for 100 sense-response cycles has taken place. The three types of neurons in Mona,
shown in separate partitions, are: (1) Receptors that sense environmental information, (2) Motors
that express responses to the environment, and (3) Mediators that connect other neurons
(including other mediators), in a cause and effect manner.
Figure 9 – Neural Network
The environment and neural network form a complementary system. The environment outputs a
“current” stimulus that can be sensed by a receptor neuron. The neural network then produces a
response that is used by the environment to probabilistically produce another stimulus. The
initial stimulus is a null value not sensed by any receptor. When Stimulus 0 is sensed, the goal
has been achieved. Motor neurons output responses that deterministically produce cause (but not
effect) stimuli in the environment, thus allowing the learner to “navigate”. After a cause stimulus
is reached, the learner may attempt to produce its associated effect by issuing a “wait” response,
not associated with a motor neuron. At that time, the environment determines, according to
accumulated probabilities, whether to make the associated effect stimulus current. For example,
in the environment and neural network shown above, the optimal response sequence is: produce
Stimulus 3 via Motor 3, wait for Stimulus 2 with .62 probability, then having fired Context 1,
wait again the the goal with .91 probability. Issuing a response from Motor 5 would be
counterproductive, since it lowers the probability of achieving the goal through Context 4. The
numbers shown in the Mediator symbols in the neural network are enablements that correspond
to learned probabilities: Mediator 4 = .88, and Mediator 1 = .68. Mediator 6 has been learned to
be ineffective in goal-seeking.
As previously mentioned, responses are motivated in Mona via a propagation scheme
originating from goal states and accumulating in motor neurons. A response is probabilistically
selected based on the weighted motivation values resident in motor neurons. In this study,
Receptor 0 is always associated with a positive goal value. During motivation propagation, an
highly enabled mediator, that is, one with a high probability of success, shunts motivation to its
cause neuron; the rationale being that the mediator’s cause-effect transition will succeed if its
cause neuron can be influenced to fire. A mediator with a low enablement shunts motivation to
higher mediators for which it is an effect, thus influencing them to produce responses that enable
the mediator. The enablement accumulation is somewhat analogous to the probability
accumulation in the environment. When the cause of an enablement mediator fires, motivation is
shunted to influence its effect to occur.
Learning is based on a wager/payoff scheme. When its cause fires, a mediator expresses a
“stake” in its effect firing by issuing a wager on that neuron. The magnitude of the wager is
proportional to the firing strength of the cause. Motor and receptor neurons fire at a constant
strength of 1.0. A mediator’s firing strength is a product of its effect’s firing strength and size of
the wager on the effect. Wagers from higher level mediators iteratively propagate to effect
neurons, carrying an enabling influence. If the effect neuron fires, the mediator is rewarded in
proportion to the size of its wager. Conversely, should the neuron not fire within a prescribed
time, the mediator is punished proportionately. Reward and punishment take the form of an
increase and decrease to the base enablement of the mediator, respectively. Base enablement is
roughly defined to be the conditional probability that the mediator can fire its effect given that its
cause fires. The base enablement update begins by computing a weighted wager:
weighted-wagermediator = (wagermediator)2 / (base-enablement * wagersall-mediators ) (1)
This term represents the payoff weight should the wager succeed. For example, a maximum
weighted-wager of 1.0 represents the entire base enablement of a mediator as the sole wager on
an outcome. Conversely, the payoff weight is less if other wagers are present to contribute to the
outcome or if a mediator wagers only a fraction of its base enablement. The updated enablement
is the historical sum of the successful weighted-wagers, including the current weighted-wager,
divided by the total sum:
base-enablement = weighted-wagersuccessful / weighted-wagers (2)
Initially the learner is likely to attempt to reach the goal stimulus from an immediate cause.
Doing this repeatedly forces it to learn the “true” base enablement values of these mediators. In a
probabilistic manner, the learner wagers from higher level mediators. In some networks, cause
neurons are themselves mediators. To fire them, the motivation mechanism essentially turns their
effects into secondary goals. Over sense-response iterations, base enablement values are learned
and goal-seeking becomes more efficient.
Figure 10 shows success rate plotted as number of contexts increases. A trial consists of
iterations of sense-response cycles. A successful trial is defined as reaching the goal state within
an amount of time that permits the learner to produce every elementary cause stimulus. Thus the
time is proportional to the number of contexts. For this experiment, the context probabilities
ranged from -1.0 to +1.0 inclusive. Three lines are graphed: base, learning, and random success
rates. The base rate is intended to give a notion of a non-learning task; the base enablement
values of the mediator neurons are initially set to the actual probabilities of the corresponding
contexts. A negative probability corresponds to a zero base enablement. Although these
quantities are not identical in meaning, it serves as a suitable performance baseline. The learning
success rate is the experimental result. The random rate serves as a lower baseline and is the
result of producting random valid responses. The value plotted for each context level is the
average of 100 test trial samples, each sample taken after 1000 learning trials.
% Succes
Figure 10 – Success rate with increasing contexts
The data indicates that the learner performs better as the environmental complexity, in the form
of the number of contexts, increases. The reason for this is that a greater number of contexts
often affords more pathways for success. For example, in an environment of a single context,
due to the possibility of negative probabilities, there is a 50% chance of an unreachable goal
stimulus. With more contexts, more pathways are possible to reach the goal.
Figure 11 is intended to display the effectiveness of learning. Here, whether successful or not,
the average accumulated goal-reaching probability is plotted over an increasing number of
contexts. Consistent with the findings presented in Figure 10, as contexts increase, opportunities
to accumulate positive probabilities increase.
Figure 11 – Success probability with increasing contexts
Figure 12 shows the rate of learning as a function of increasing trials for environments having 5
and 10 contexts. For this experiment, in order to accentuate the influence of conflicting contexts
on learning, all contexts directly “funnel” into the goal stimulus, i.e., all contexts have the goal
stimulus as their effect. Furthermore, only one of the contexts contributes a +1.0 probability; the
remainder contribute -1.0. Thus the task is to learn which is the positive context. Each plotted
value is the result of averaging 100 samples. The simplified task of discriminating among 5
contexts is reflected in the more rapid learning curve. At about 300 trials, however, the learner is
also able to learn the 10 context task successfully.
% Succes
Figure 12 – Success rate with increasing trials
One way of looking at the method presented here is as an extension of reinforcement learning
(Kaelbling, Littman, and Moore, 1996) 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 (Watkins, 1989) and Temporal
Difference Learning (Sutton, 1988), 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 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 lost.
The question arises as to how Mona differs from more conventional, e.g. feedforward, artificial
neural networks. A recurrent network could plausibly be trained to recognize input patterns
representing the existence of contexts and to associate these with response sequences. The most
important distinction 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.
The described technique allows Mona to successfully learn environmental contexts in the
abstracted sense defined here. The expectation is that this will carry-over to more general
learning situations. This work is viewed as a progress step, supplying a key piece of
infrastructure necessary for more elaborate learning tasks. The ability to create new mediator
neurons by hypothesizing cause and effect relationships, including those involving logical
conjunctions among multiple causes, is being designed as part of a maze-learning task. As an
example of a conjunction, a door might not open unless a code is entered and a key is used. In
this case, a logical and relationship exists between the two causal events. The maze-learning task
also involves the ability to learn inhibiting influences, which is essential to many tasks.
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... For the purpose of comparison we use the standard LSTM library. To ensure that we set optimally its numerous parameters, we repeat some of the published results [117][118][119], where the performance of the LSTM model is compared to the performance of the Q-learner and Elman network. The experiments presented here are an adaptation of those proposed in literature [117][118][119]. ...
... To ensure that we set optimally its numerous parameters, we repeat some of the published results [117][118][119], where the performance of the LSTM model is compared to the performance of the Q-learner and Elman network. The experiments presented here are an adaptation of those proposed in literature [117][118][119]. The task is to learn the shortest path to the goal starting from any valid position in a discrete maze world. ...
... I have previously conducted research into a number of issues that differentiate conventional AI from natural intelligence. These include context, motivation, plasticity, modularity, instinct, and surprise (Portegys 2007(Portegys , 2010(Portegys , 2013(Portegys , 2015. Morphognosis, in particular, has been previously applied to the task of nest-building by a species of pufferfish (Portegys 2019). ...
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This book has two purposes. First, its intent is to inform the public about the subject of Attificial Intelligence, not from the perspective of a science-oriented journalist, who may or may not understand what he or she has seen and read, but from the viewpoint of one who is involved deeply in the subject. Second, it seems important to ponder the reasons why this obscure field has hit the front pages. The public has discovered AI but according to the author, is not quite sure what it is. The book tries to address 3 questions; 1. What do we have to know about computers in order to live in a world that is full of them. 2. What can we learn about what it means to be intelligent through our development of computers that can understand. 3. How will intelligent computers affect the world we live in.
This paper surveys the field of reinforcement learning from a computer-science perspective. It is written to be accessible to researchers familiar with machine learning. Both the historical basis of the field and a broad selection of current work are summarized. Reinforcement learning is the problem faced by an agent that learns behavior through trial-and-error interactions with a dynamic environment. The work described here has a resemblance to work in psychology, but differs considerably in the details and in the use of the word ``reinforcement.'' The paper discusses central issues of reinforcement learning, including trading off exploration and exploitation, establishing the foundations of the field via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state. It concludes with a survey of some implemented systems and an assessment of the practical utility of current methods for reinforcement learning. Comment: See for any accompanying files
The principles of recency and contiguity are two cornerstones of the theoretical and empirical analysis of human memory. Recency has been alternatively explained by mechanisms of decay, displacement, and retroactive interference. Another account of recency is based on the idea of variable context (Estes, 1955; Mensink & Raaijmakers, 1989). Such notions are typically cast in terms of a randomly fluctuating population of elements reflective of subtle changes in the environment or in the subjects' mental state. This random context view has recently been incorporated into distributed and neural network memory models (Murdock, 1997; Murdock, Smith, & Bai, 2001). Here we propose an alternative model. Rather than being driven by random fluctuations, this formulation, the temporal context model (TCM), uses retrieval of prior contextual states to drive contextual drift. In TCM, retrieved context is an inherently asymmetric retrieval cue. This allows the model to provide a principled explanation of the widespread advantage for forward recalls in free and serial recall. Modeling data from single-trial free recall, we demonstrate that TCM can simultaneously explain recency and contiguity effects across time scales.
Neural models assist in characterizing the processes carried out by cortical and hippocampal memory circuits. Recent models of memory have addressed issues including recognition and recall dynamics, sequences of activity as the unit of storage, and consolidation of intermediate-term episodic memory into long-term memory.