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The role of self-organization for the
synthesis and understanding of behavioral
systems
CHAPTER I. of
EVOLUTIONARY ROBOTICS
Stefano Nolfi and Dario Floreano
Naveen Suresh Kuppuswamy
January 10, 2007
Robot Intelligence Technology Lab.
CONTENTS
1.
Introduction
a.
b.
c.
Behavior based robotics
Robot Learning
Artificial Life
2. Engineering Perspective
3. Ethological Perspective
4. Biological Perspective
a.
b.
c.
Incremental Evolution
Extracting supervision from environment through lifetime learning
Development and evolution of evolvability
5. Conclusions
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1. INTRODUCTION

The basic idea
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1.a. Behavior Based Robotics
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Robot is provided with a collection of simple basic behaviors,
global behavior emerges through the interaction between these
and environment.
Coordination of behaviors maybe competitive or cooperative
Design is by Trial and Error process
Breakdown into simpler behaviors is done by designer
intuitively unlike evolutionary robotics which is self-organised.
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1.b.Robot Learning

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Control systems (neural networks) trained with incomplete data and
then generalize acquired knowledge to new circumstances.
Neural systems either perform mapping between sensory input and
motor state, or develop subsystems of controllers.
Different learning algorithms provide different constraints on
architecture and supervision required by designer.
Evolutionary robotics is similar since it’s a form of learning but
differs in following respects


Amount of supervision is much lower
No constraints on what can be part of the
self-organisation process
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1.c. Artificial Life
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Artificial life tries to understand all phenomena though their
reproduction in artificial systems (usually as a simulation on a
computer)
Relies on theory of complex dynamical systems (global
properties at one level emerge from the interaction of a number
of similar elements at lower elements).
Evolutionary robotics is similar but uses physical devices
instead of simulations
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2. Engineering Perspective

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Behavioral systems such as robots are difficult to design
Behavior is the emergent property of motor interaction with the
environment
While simple robots can produce complex behavior, its difficult
to predict which rules produce a given behavior.
Conventional strategy: divide and conquer (breakdown in to
perception, planning and action)
Brooks approach : desired behavior broken in simpler basic
behaviors modulated through a coordination mechanism
The latter is more successful but still designer must decide on
breaking down.
Evolutionary Robotics treat system as a whole and its global
behavior thus releasing burden on designer.
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2. Engineering Perspective (contd.)
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3. Ethological Perspective
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4. Biological Perspective

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Evolutionary robotics and biology have the common interest in
trying to understand the success of natural evolution.
Evolutionary Robotics concerns itself with identifying the
conditions under which evolutionary process may select right
individuals
This is done through

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Incremental evolution through competition between species.
Extracting supervision from the environment through lifetime learning
Including genotype to phenotype mapping.
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4.a.Incremental Evolution

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An issue is how artificial evolution can select individuals which
have competencies to solve complex tasks.
For simpler tasks this can be solved by designing a suitable
fitness criterion.
For complex problems this is not possible because of bootstrap
problem.
One solution is to increase the amount of supervision
Another is to start the evolutionary process with a simplified
version of the task and progressively increase complexity by
modifying selection criteria – incremental evolution.
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4.d Extracting Supervision from the environment
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External environment does not provide direct cue on how agent
should act to achieve a goal
Individuals maybe supervised richly but less explicitly
(ontogenetic) or evaluated only once on how adapted they were
throughout their lifetime (phylogenetic).
In ontogenetic evolution information is received from
environment throughout lifetime, but this huge amount of
information can only indirectly transformed into a measure of
how agent is doing.
Individuals may not start with a general strategy but adapt
throughout lifetime (plastic general) or they might have a
general strategy suitable for all environments (full general).
Full general is preferable but not always possible.
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4.e. Development and evolution of evolvability
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For adaptation systems must posses evolvability (ability of
random variation to sometimes produce improvements).
This depends on representation problem or genotype-tophenotype mapping problem.
It may be a simple one-to-one (one gene encoding a single
character) or

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complex with several levels of organization
Growing recursive instructions
Plasticity
Genotypes that vary in length.
This mapping problem is still not clear and its not yet possible
to convert this problem itself to evolutionary process.
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6. CONCLUSIONS
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The main characteristic making evolutionary robotics suitable
for study of adaptable behavior is the reliance on selforganization.
From an Engineering point of view simplifies a designers job.
From a point of view of study of natural systems it may help us
understand how natural organisms produce adaptive behavior.
By scaling up to more complex tasks we may be able to explain
the emergence of extraordinary variety of life-forms present on
our planet.
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