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2007. 01. 16 RIT Lab. Yehoon Kim Ch.6 Beyond Reactive Intelligence Contents Introduction Evolving a Garbage Collection Robot Robots with an Internal Dynamics Re-adaptation to Layout Variations Emergence of Complex Behaviors Conclusion Introduction

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2007 01 16 rit lab yehoon kim l.jpg

2007. 01. 16

RIT Lab.

Yehoon Kim

Ch.6 Beyond Reactive Intelligence


Contents l.jpg
Contents

  • Introduction

  • Evolving a Garbage Collection Robot

  • Robots with an Internal Dynamics

  • Re-adaptation to Layout Variations

  • Emergence of Complex Behaviors

  • Conclusion


Introduction l.jpg
Introduction

  • Modular architectures and internal dynamical states

  • Garbage collection task

    • Modular neural controllers outperform simple non modular ones

    • Analyzing the internal organization of evolved individuals

  • Homing navigation

    • Robot navigate an arena with a limited, but rechargeable, energy supply

  • Discussion of complex behaviors


Evolving a garbage collection robot l.jpg
Evolving a Garbage Collection Robot

  • Will architectural modules correspond to basic behaviors?

  • The robot and environment

    • Goal : clean the arena

  • Control architectures

(a) feedforward

(b) internal layer of hidden units

(d) modular with two pre-designed modules

(c) recurrent


Evolving a garbage collection robot5 l.jpg
Evolving a Garbage Collection Robot

  • Emergent modular architecture

    • The number of modules, the combination of modules, and the weights of the modules are evolved while the robot interacted with the environment.

    • The first output neuron : determines the state of the corresponding effector

    • The second output neuron (selector) : competes with the selector neuron of the two modules

(e) Emergent modular


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Evolving a Garbage Collection Robot

  • Evolutionary process

    • carried out in simulation using the sampling technique in Ch.3

  • Score of robot

    • by counting the number of objects correctly released outside the arena


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Evolving a Garbage Collection Robot

  • Distal behaviors

    • look for a target while avoiding walls

    • look for a wall while avoiding targets, etc.

  • Can we find in evolved individuals a correspondence between distal behaviors? No.

    • difficult to understand what is going on when many competing neural modules are involved


Robots with an internal dynamics l.jpg
Robots with an Internal Dynamics

  • Accumulated fitness value

    Φ= V (1-i)

    where 0 < V < 1

    0 < i < 1

     maximizing the speed and obstacle avoidance

     straight motion is removed in Ch.4


Robots with an internal dynamics9 l.jpg
Robots with an Internal Dynamics

  • Neural network

  • Average population fitness


Robots with an internal dynamics10 l.jpg
Robots with an Internal Dynamics

  • Visualization of the hidden node activations Light on Light off


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Re-adaptation to Layout Variations

  • Testing the generalization properties

    (a) Test in training condition

    (b) The battery is not automatically recharged

    (c) The light source is positioned on the top right corner

(a) (b) (c)


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Re-adaptation to Layout Variations

  • Re-adaptation with a new light position


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Emergence of Complex Behaviors

  • Two methods for developing behaviors through evolution

    • Detailed specification of the fitness function

    • The fitness measure not as a detailed and complex function

  • Bootstrap problem


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Conclusions

  • Structure of the evolving controller allows the development of complex behaviors

  • Evolutionary modularity is a promising approach to the development of complex abilities

  • Artificial evolution can develop mechanisms that go beyond reactive behavior when this is necessary without being explicitly told to do so


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