1. 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
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2. CONTENTS Introduction 2. Engineering Perspective
Behavior based robotics
Robot Learning
Artificial Life
2. Engineering Perspective
3. Ethological Perspective
4. Biological Perspective
Incremental Evolution
Extracting supervision from environment through lifetime learning
Development and evolution of evolvability
5. Conclusions
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3. 1. INTRODUCTION
The basic idea
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4. 1.a. Behavior Based Robotics
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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5. 1.b.Robot Learning
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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6. 1.c. Artificial Life
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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7. 2. Engineering Perspective
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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8. 2. Engineering Perspective (contd.)
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9. 3. Ethological Perspective
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10. 4. Biological Perspective
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
Incremental evolution through competition between species.
Extracting supervision from the environment through lifetime learning
Including genotype to phenotype mapping.
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11. 4.a.Incremental Evolution
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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12. 4.d Extracting Supervision from the environment
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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13. 4.e. Development and evolution of evolvability
For adaptation systems must posses evolvability (ability of random variation to sometimes produce improvements).
This depends on representation problem or genotype-to-phenotype mapping problem.
It may be a simple one-to-one (one gene encoding a single character) or
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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14. 6. CONCLUSIONS
The main characteristic making evolutionary robotics suitable for study of adaptable behavior is the reliance on self-organization.
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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