Associative Learning for a Robot Intelligence
by John H Andreae
Imperial College Press, 1998
The explanation of brain functioning in terms of the
association of ideas has been popular since the 17th century. However, recently
the process of association has been dismissed as computationally inadequate
by prominent cognitive scientists. In this book, a sharper definition of the
term "association" is used to revive the process by showing that associative
learning can indeed be computationally powerful. With an appropriate
organization, associative learning can be embodied in a robot to realize a
human-like intelligence, which sets its own goals, exhibits unique
unformalizable behaviour and has no hidden homunculi.
A series of simulated robot experiments demonstrates Turing
Machine power, a simple form of internal "thinking", incipient communication of
intentions, control of a hierarchical task, the emergence of cooperation, and
some preliminary steps towards the learning of language. The "innate"
endowment of the robot includes the association templates, which drive the
associative learning, and a new form of short-term visual memory.
Some say that Artificial Intelligence is undergoing a
paradigm shift. Certainly there are several competing ideas and ideals. Neural
networks and dynamic systems are offered as alternatives to the information
processing and digital computer models of the brain. We are asked to decide
between symbolic and subsymbolic, between algorithmic and nonalgorithmic,
and between information processing and interactive systems. Even in the short
distance travelled in this book, associative learning is seen to embrace both
sides of these dichotomies.
Contents of Book
0 Preamble
1 Chapter 1 Associative Learning
2 Chapter 2 The BunPie Microworld
3 Chapter 3 Designing Templates
4 Chapter 4 Numbers in the Head
5 Chapter 5 Universal Turing Machine
6 Chapter 6 Communicating Intentions
7 Chapter 7 Consciousness before Language
8 Chapter 8 An Hierarchical Task
9 Chapter 9 Stress and Disapproval
10 Chapter 10 Painted Vision
11 Chapter 11 Cooperation
12 Chapter 12 Turn-Taking
13 Chapter 13 Climbing a Tree or Building a Rocket
R References
A Appendix A. Implementation of PURR-PUSS
B. A Brief Historical Note about STeLLA
I Index
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Overview of Chapter 1
Associative learning is defined in terms of associations and association
templates. The definitions are justified in terms of simple neural networks.
The basics of associative learning are illustrated in a robot brain, called
PURR-PUSS (PP). The structure of PP is justified in terms of a finite-state
automaton with short-term and long-term memories.
PP has sufficient computational power and acquires its own top level program
as a growing collection of associations. The system avoids the limitations of
formal systems by close interaction with an open world.
This book aims to convince the reader that associative learning is a
promising explanation of brain processing, in spite of authoritative claims to
the contrary.
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Overview of Chapter 2
The PURR-PUSS (PP) system is used to suggest how associative learning might
be exploited to develop a robot intelligence. Four claims are made for PP: It
becomes an individual through learning, it sets its own goals, it is
computationally powerful, and it escapes the limitations of formal systems.
The learning of associations, motivation through novelty and reward, and
the construction of plans are explained in terms of PP's actions and stimuli
in the BunPie microworld.
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Overview of Chapter 3
This chapter is a miscellany of topics, which will be referred to later in
the book. It is suggested that you skip the chapter on a first reading and
return to it as necessary.
Event-sustainability provides conditions for a set of templates to be used,
particularly in making plans.
Generalization and discrimination are shown to be fundamental consequences
of some associations having larger contexts than others.
The concept of an event in a context is extended to include threading
events, predictions and even whole associations. New types of association
template use these event-types and also include the important replacement
association. These templates make possible different ways of holding and
changing information.
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Overview of Chapter 4
In this experiment associations combine to support the behaviour of a robot
in a simple microworld.
When the real stimuli from the microworld disappear, predicted stimuli take
over in the formation of associations. This allows the behaviour to become
internalized.
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Overview of Chapter 5
The aim of this chapter is to establish formally the computational power of
associative learning by showing that a Universal Turing Machine can be learned.
The cut down PP system learns the whole of the UTM including its extendable
tape. Two special features of the demonstration are the five parallel streams
of events and the teaching in parts. The demonstration illustrates the
interweaving of associations to support the parallel streams. The teaching in
parts enables the whole system to be taught before letting it emulate the UTM
entirely on its own.
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Overview of Chapter 6
In her book "The Emergence of Symbols", Elizabeth Bates (1979) describes a
paradigmatic experiment to demonstrate what she refers to as "the First Moment"
in the dawn of language. The experiment is so beautifully simple that I was
tempted to try it out with PURR-PUSS. Not only was there here a well-defined
experiment but also a careful analysis of the results. Furthermore, Elizabeth
Bates saw this as the first of "two critical moments in the dawning of human
communication through symbols". What better place could there be to start on
the hazardous journey of giving PP language!
The plans of PP play a central role in the experiment of this chapter.
Unrealized plans led to a build-up of Frustration until PP cried. The plans
revealed what PP wanted to happen and we could check the plans against PP's
memory at the time. This is a luxury that psychologists studying infants do
not have.
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Overview of Chapter 7
A future buyer of a conscious intelligent robot who wants the robot to carry
out a task on its own must convince herself of the robot's competence and
intentions.
A series of increasingly demanding definitions of robot behaviour are
constructed with the needs of the robot buyer in mind. The series goes far
beyond current progress with the PP system and indicates a route for future
research.
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Overview of Chapter 8
The 5-puzzle experiment shows how an auxiliary action, called `RaiseEyebrows',
could be used to process a task with nested subtasks. The processing of nested
subtasks is analogous to the processing of nested clauses in language or nested
subroutines in programming.
Each time a subtask is entered, the name of the subtask is associated with
the level of the subtask and the auxiliary action. At the end of the subtask
(even if lower subtasks have been processed in the meantime), the name of the
subtask is recovered and the subtask is continued.
Using soft teaching, the teacher needs to provide the auxiliary action just
once for each task level, the first time that that task level is entered. After
that, PP chooses the auxiliary action itself.
The next chapter demonstrates a much more acceptable auxiliary action for
teacher to use when teaching nested clauses. This auxiliary action is the
addition of stress to sounds, which is a common feature of ordinary speech.
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Overview of Chapter 9
The aim of this chapter is twofold.
First, a small memory task is employed to show how stress on sounds can be
used as an auxiliary action in soft teaching. This auxiliary action would be
more appropriate for teaching language structure than the RaiseEyebrows
auxiliary action used for the 5-puzzle task in the last chapter.
Secondly, the same memory task is used to demonstrate how disapproval
operates to distinguish those associations responsible for an unwanted action
from other associations active at the same time. In brief, disapproval of an
action of PP causes all associations active at the time to be marked. When PP
then goes through the same situation with the correct action and no disapproval,
the still active (and therefore wanted) associations lose their disapproval
marks.
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Overview of Chapter 10
PP is given a special memory, called Painted Vision, which holds what it has
recently seen in the space around it. The memory decays with time and is
refreshed by PP's visual stimuli.
This chapter describes this memory and how it works. Then the
stimulus-predicting templates are given for PP in the BunPie Microworld, so the
contribution of Painted Vision can be emphasised. Finally, saccades are
introduced to induce refreshing of the Painted Vision when something moves in
peripheral vision.
Painted Vision is seen as a spatial equivalent of STM.
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Overview of Chapter 11
Here we see the final runs of the BunPie Microworld with the two PP robots
learning to cooperate. In the smallest 2x2 Microworld, the two PP robots lock
into a cycle of perpetual cooperation some number of steps from the start. On
different runs, different cycles are entered after different numbers of steps.
If the robots were disturbed from one of these cycles they would be no better
at cooperating than they were when they entered the cycle, so the situation
seen in the 2x3 world, where there is a gradual increase in cooperative
resources but no locked in cycle, is more robust and more deserving of the
description `emergent behaviour'.
Until the robots enter a cycle and become like automata, they are acting on
their own and are driven largely by their own novelty goals. We see how the PP
robot can escape the limits of a formal system.
In the last part of the chapter, the two robots are separated by a wall and
it is shown how cooperation can be supported by communication between the
robots: each robot makes sounds and hears those made by the other robot.
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Overview of Chapter 12
One robot is taught a simple counting task using ten fingers. It is then
shown that another robot having the same association templates can learn this
task from the first robot. A third robot has one of its templates lacking an
event-type. When we let this third robot interact with the first robot, it can
learn only part of the counting task.
With one robot learning from another it is important that they take turns
in a disciplined way. To make this possible, the robots are provided with a
turn-taking reflex controlled by a turn-taking frustration level.
The aim of the experiment is to illustrate the (obvious?) fact that robots
can learn best from each other if they each have the same set of association
templates.
The final section demonstrates a mechanism by which one robot can learn
from imitating another.
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Final Paragraph of Chapter 13
Dissatisfaction is a driving force for innovation and research. The
collection of experiments described in this book is incomplete in every way.
It is hoped that their incompleteness will draw others into this research area.
The taboo on associative learning research must be lifted. It owes nothing to
behaviourism. You have seen a little of what associative learning can do.
Contrary to Pinker (1997), quoted at the beginning of Chapter 5, to Fodor (1994),
quoted at the beginning of Chapter 8, and to Jackendoff (1992), quoted at the
beginning of this chapter, the process of association is neither underpowered
nor hopeless nor inadequate:
Associative learning is potent and promising!
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