References

Last Updated 5/19/04

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• Aharonov-Barki, R., Beker, T., Ruppin, E. (1999).  Spontaneous Evolution of Command Neurons, Place Cells and Memory Mechanisms in Autonomous Agents.  In Advances in Artificial Life, ECAL '99, vol. 1674, Lecture Notes in Artificial Intelligence. Springer Verlag.
  
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• Beer, R.D., Gallagher, J.C. (1992).  Evolving dynamical neural networks for adaptive behavior.  Adaptive Behavior, 1, 1, 92-122.
  
  Blynel, J. (2003).  Evolving Reinforcement Learning-Like Abilities for Robots.  In A. Tyrrell, P.C. Haddow, and J. Torresen: Evolvable Systems: From Biology to Hardware: 5th International Conference, ICES 2003.
  
  Blynel, J., Floreano, D. (2002).  Levels of dynamics and adaptive behavior in evolutionary neural controllers.  In Hallam, B., Floreano, D., Hallam, J., Hayes, G., Meyer, J.A. (Eds.), From Animals to Animats 7: Proceedings of the Seventh International Conference on Simulation of Adaptive Behavior.  Cambridge, MA: MIT Press.
  
  

• Dietterich, T., (2000).  Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition. Journal of Artificial Intelligence Research, 13, 227-303.
  
  Doya K., Samejima K., Katagiri K., Kawato M. (2002). Multiple model-based reinforcement learning. Neural Computation, 14, 1347-1369
  
  
  

• Edwards, D. H., Heitler, W. J., Krasne, F. B. (1999).  Fifty years of a command neuron: the neurobiology of escape behavior in the crayfish.  Trends in Neurosciences, 22(4), 153-161.
  
  
  

• Fan, J., Yao, Q., Tong, H. (1996).  Estimation of Densities and Sensitivity Measures in Nonlinear Dynamical Systems.  Biometrika, 83, 1, 189-206.
  
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  Fujimoto, K., Kaneko, K. (2003).  How Fast Elements can Affect Slow Dynamics.  Physica D: Nonlinear Phenomena, 180, 1-2.
  
  
  

• Giszter, S. F., Mussa-Ivaldi, F. A., Bizzi, E. (1993).  Convergent force fields organized in the frog's spinal cord.  Journal of Neuroscience, 13, 2, 467-491.
  
  Goldberg, D.E. (2002).  The Design of Innovation: Lessons from and for Competent Genetic Algorithms.  Boston, MA: Kluwer Academic Publishers.
  
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• Harvey, I., Husband, P., Thompson, A., and Jakobi, N. (1997).  Evolutionary Robotics: the sussex approach.  Robotics and Autonomous Systems, 20, 205-224.
  
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  Hochreiter, S., Schmidhuber, J. (1997).  Long short-term memory.  Neural Computation, 9, 8, 1735-1780.
  
  
  

• Ijspeert, A., Nakanishi, J., Schaal, S. (2003).  Learning attractor landscapes for learning motor primitives.  In: Becker, S., Thrun, S., Obermayer, K. (Eds.), Advances in Neural Information Processing Systems, 15. Cambridge, MA: MIT Press.
  
  

• Jacobs, R., Jordan, M., Nowlan, S., Hinton, G. (1991). Adaptive mixtures of local experts.  Neural Computation, 3,1, 79-87.
  
  Jordan, M., Jacobs, R. (1994).  Hierarchical mixtures of experts and the EM algorithm.  Neural Computation, 6,2, 181-214.
  
  
  

• Kawato, M., Furukawa, K., Suzuki, R. (1987).  A hierarchical neural network model for the control and learning of voluntary movement.  Biological Cybernetics, 57, 169-185.
  
  
  

• Mataric, M. J. (2002).  Sensory-motor primitives as a basis for imitation: Linking perception to action and biology to robotics.  In Dautenhahn, K., Nehaniv, C. L. (Eds.), Imitation in Animals and Artifacts (pp. 391-422). Cambridge, MA:  MIT Press.
  
  Mesot, B., Sanchez, E., Pena, C., Perez-Uribe, A. (2002).  SOS++: Finding Smart Behaviors Using Learning and Evolution.  In Standish, Abbass, Bedan (Eds.), Artificial Life VIII, MIT Press, pp. 264-273.
  
  McCloskey, M., Cohen, N. J. (1989).  Catastrophic interference in connectionist networks:  The sequential learning problem.  The Psychology of Learning and Motivation, 24, 109-165.
  
  McGovern, A., Barto, A. G. (2001).  Accelerating Reinforcement Learning through the Discovery of Useful Subgoals.  Proceedings of the 6th International Symposium on Artificial Intelligence, Robotics, and Automation in Space: i-SAIRAS.
  
  McGovern, A., Sutton, R. S., Fagg, A. H. (1997).  Roles of macro-actions in accelerating reinforcement learning.  Proceedings of the 1997 Grace Hopper Celebration of Women in Computing, 13-18.
  
  Mitchell, M. (1998).  An Introduction to Genetic Algorithms.  Cambridge, MA: MIT Press.
  
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• Needlman, R.D. (2003).  Growth and Development.  In Behrman, R. E., Kliegman, R. M., Jenson, H. B. (Eds.),
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  Ninokura, Y., Mushiake, H., Tanji, J. (2003).  Integration of Temporal Order and Object Information in the Monkey Lateral Prefrontal Cortex.  Journal of Neurophysiology, 10, 1152.
  
  Nishimoto, R., Tani, J. (2003).  Learning to Generate Combinatorial Action Sequences Utilizing the Initial Sensitivity of Deterministic Dynamical Systems.  Proc. of the 7th International Work-Conference on Artificial and Natural Neural Networks (IWANN'03).  Springer, pp. 422-429.
  
  Nolfi, S., Floreano, D. (2000).  Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines.  Cambridge, MA: MIT Press.
  
  Nolfi, S., (2002).  Evolving Robots able to Self-localize in the Environment: The Importance of Viewing Cognition as the Result of Processes Occurring at Different Time Scales.  Connection Science, 14, 3, 231-244.
  
  
  

• Parr, R., Russell, S. (1998).  Reinforcement Learning with Hierarchies of Machines.  In: Jordan, M. I., Kearns, M. J., Solla, S. A., (Eds.), Advances in Neural Information Processing Systems, 10. Cambridge, MA: MIT Press, pp. 1043-1049.
  
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• Sakai, K., Kitaguchi, K., Hikosaka, O. (2003).  Chunking during human visuomotor sequence learning.  Experimental Brain Research, 152, 2, 229-242.
  
  Schaal, S. (1999).  Is imitation learning the route to humanoid robots?  Trends in Cognitive Sciences, 3, 233-242.
  
  Siegelmann, H.T., Sontag, E.D.(1995).  On the computational power of neural nets.  Journal of Computer and System Sciences, 50, 1, 132-150.
  
  Smith, T., Husbands, P., Philippides, A., O'Shea, M. (2002).  Temporally Adaptive Networks:  Analysis of GasNet Robot Control Networks.  In Standish, Abbass, Bedan (Eds.), Artificial Life VIII, MIT Press, pp. 274-282.
  
  Sun, R., Merrill, E., Peterson, T. (2001).  From Implicit Skills to Explicit Knowledge: A Bottom-Up Model of Skill Learning.  Cognitive Science, 25, 2, 203-244.
  
  Sutton, R., Precup, D., Singh, S. (1999).  Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning.  Artificial Intelligence, 112, 181-211.
  
  
  

• Tani, J. (2003).  Learning to generate articulated behavior through the bottom-up and the top-down interaction processes.  Neural Networks, 16, 1, 11-23.
  
  Tani, J., Nolfi, S. (1999).  Learning to perceive the world as articulated: An approach for hierarchical learning in sensory-motor systems.  Neural Networks, 12, 1131-1141.
  
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  Teyke, T., Weiss, K. R., Kupfermann, I. (1990).  An identified neuron (CPR) evokes neuronal responses reflecting food arousal in Aplysia.  Science, 247, 85-87.
  
  Thoroughman, K. A., Shadmehr, R. (2000).  Learning of action through combination of motor primitives.  Nature, 407, 742-747.
  
  Tucci, E., Quinn, M., Harvey, I. (2002).  An Evolutionary Ecological Approach to the Study of Learning Behavior Using a Robot-Based Model.  Adaptive Behavior, 10, 3/4, 201-222.
  
  
  

• Werbos, P. (1990).  Backpropagation through time: what it does and how to do it.  Proc. IEEE, 78, 1550-1560.
  
  Wolpert, D. M., Kawato, M. (1998).  Multiple paired forward and inverse models for motor control.  Neural Networks, 11, 1317-1329.
  
  
  

• Yamauchi, B., Beer, R.D. (1994).  Sequential behavior and learning in evolved dynamical neural networks.  Adaptive Behavior, 2, 3, 219-246.
  
  
  

• Ziemke, T., Thieme, M. (2002).  Neuromodulation of Reactive Sensorimotor Mappings as a Short-Term Memory Mechanism in Delayed Response Tasks.  Adaptive Behavior, 10, 3/4, 185-199.