Continuous and Embedded Learning in Autonomous Vehicles : Adapting to Sensor

@inproceedings{Schultza2000ContinuousAE,
  title={Continuous and Embedded Learning in Autonomous Vehicles : Adapting to Sensor},
  author={FailuresAlan C. Schultza and John J. GrefenstettebaIntelligent},
  year={2000}
}
  • FailuresAlan C. Schultza, John J. GrefenstettebaIntelligent
  • Published 2000
This project describes an approach to creating autonomous systems that can continue to learn throughout their lives, that is, to be adaptive to changes in the environment and in their own capabilities. Evolutionary learning methods have been found to be useful in several areas in the development of autonomous vehicles. In our research, evolutionary algorithms are used to explore alternative robot behaviors within a simulation model as a way of reducing the overall knowledge engineering eeort… CONTINUE READING
Highly Cited
This paper has 21 citations. REVIEW CITATIONS

Citations

Publications citing this paper.
Showing 1-10 of 13 extracted citations

Continuous and Embedded Learning for Multi-Agent Systems

2006 IEEE/RSJ International Conference on Intelligent Robots and Systems • 2006

Towards Robust Skill Learning With Prediction Guided Autonomy in Unknown Environments

2006 IEEE Mountain Workshop on Adaptive and Learning Systems • 2006
View 1 Excerpt

Anytime coevolution of form and function

IEEE Congress on Evolutionary Computation • 2003

References

Publications referenced by this paper.
Showing 1-10 of 14 references

Evolution of homing navigation in a real mobile robot

IEEE Trans. Systems, Man, and Cybernetics, Part B • 1996

Genetic learning for adaptation in autonomous robots," in Robotics and Manufacturing

J. J. Grefenstette
Recent Trends in Research and Applications, • 1996

Robo-shepherd: Learning complex robotic behaviors," in Robotics and Manufacturing

A. C. Schultz, J. J. Grefenstette
Recent Trends in Research and Applications, • 1996

Similar Papers

Loading similar papers…