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Ch.6 Beyond Reactive Intelligence

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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Ch.6 Beyond Reactive Intelligence

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  1. 2007. 01. 16 RIT Lab. Yehoon Kim Ch.6 Beyond Reactive Intelligence

  2. Contents • Introduction • Evolving a Garbage Collection Robot • Robots with an Internal Dynamics • Re-adaptation to Layout Variations • Emergence of Complex Behaviors • Conclusion

  3. 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

  4. 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

  5. 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

  6. 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

  7. 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

  8. 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

  9. Robots with an Internal Dynamics • Neural network • Average population fitness

  10. Robots with an Internal Dynamics • Visualization of the hidden node activations Light on Light off

  11. 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)

  12. Re-adaptation to Layout Variations • Re-adaptation with a new light position

  13. 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

  14. 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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