Jim Crutchfield | Computational Mechanics | Dynamics of Learning | Evolving Cellular Automata | Evolutionary Dynamics



Research Themes


Research Communications


Tools and Resources

Learning in Autonomous Robot Collectives


  • Groups Elsewhere Doing Related Work: 
    • Lerman, Mataric, Cybenko, Wolpert and Tumer, etc. 

  • Related Papers from Other Groups: 
  1. Paul Algoet. Universal schemes for learning the best nonlinear predictor given the infinite past and side information. IEEE Transactions on Information Theory, IT-45:1165-1185, 1999. 
  2. Paul H. Algoet. The strong law of large numbers for sequential decisions under uncertainty. IEEE Transactions on Information Theory, IT-40:609-633, 1994. 
  3. E. Barnard. Temporal-difference methods and markov models. IEEE Transactions on Systems, Man, and Cybernetics, 23(2):357-365, 1993. 
  4. P. Dayan. The convergence of TD( ) for general  . Machine Learning, 8(3/4):341-362, 1992. 
  5. P. Dayan and T. Sejnowski. TD( ) converges with probability 1. Machine Learning, 14:295-301, 1994. 
  6. Joerg Denziger and Michael Kordt. Evolutionary on-line learning of cooperative behavior with situation-action-pairs. (In Durfee, below)
  7. Ed Durfee, editor. Fourth International Conference on MultiAgent Systems, Piscataway, New Jersey, 2000. IEEE. 
  8. Vanathi Gopalakrishnan and Bruce Buchanan. Representing and learning temporal relationships among experimental variables. In Hayes et al., pages 148-155. http://www.cs.pitt.edu/ ~ vanathi/. 
  9. Laszlo Gyorfi, Gabor Lugosi, and Gustav Moravai. A simple randomized algorithm for sequential prediction of ergodic time series. IEEE Transactions on Information Theory, IT-45:2642-2650, 1999. 
  10. Patrick Hayes, Lina Khatib, and Robert Morris, editors. Fifth International Workshop on Temporal Representation and Reasoning, 1998. 
  11. Manfred Huber and Roderic A. Grupen. Prior structure for on-line learning. In Koivo and Lee, pages 124-129.
  12. Antti Koivo and Sukhan Lee, editors. IEEE International Symposium on Computational Intelligence in Robotics and Automation, Piscataway, New Jersey, 1997. IEEE. 
  13. Steffen Lange and Thomas Zeugmann. Inceremental learning from positive data. Journal of Computer and System Sciences, 53:88-103, 1996. 
  14. Giles C. Lee, Steve Lawrence, and Ah Chung Tsoi. Noisy time series prediction using recurrent neural networks and grammatical inference. Machine Learning, 44:161-183, 2001. 
  15. Heikki Mannila and Dimitri Rusakov. Decomposition of event sequences into independent components. In Robert Grossman, Jiawei Han, and Vipin Kumar, editors, First SIAM International Conference on Data Mining, 2001. http://www.siam.org/meetings/sdm01/html/program.htm
  16. Maciej Michalewicz and Mieczyslaw Klopotek, editors. International Conference on Intelligent Information Systems, Piscataway, New Jersey, 1999. IEEE. 
  17. Radford M. Neal and Geoffrey E. Hinton. A view of the EM algorithm that justifies incremental, sparse, and other variants. In Michael I. Jordan, editor, Learning in Graphical Models, volume 89 of NATO Science Series D: Behavioral and Social Sciences, pages 355-368, Dordrecht, 1998. Kluwer Academic. 
  18. Arlindo L. Oliveira and Joao P.M. Silva. Efficient algorithms for the inference of minimum size DFAs. Machine Learning, 44:93-119, 2001. 
  19. David Pico and Francisco Casacuberta. Some statistical-estimation methods for stochastic finite-state transducers. Machine Learning, 44:121-141, 2001. 
  20. H.S. Seung D. Haussler, M. Kearns and N. Tishby. Rigorous learning curve bounds from statistical mechanics. In Michael I. Jordan, editor, 7th Annual ACM Workshop on Computational Learning Theory, volume 89 of NATO Science Series D: Behavioral and Social Sciences, pages 76-87, Dordrecht, 1998. Kluwer Academic.