New Millennium AI and the Convergence of History: Update of 2012

Chapter
Part of the The Frontiers Collection book series (FRONTCOLL)

Abstract

Artificial Intelligence (AI) has recently become a real formal science: the new millennium brought the first mathematically sound, asymptotically optimal, universal problem solvers, providing a new, rigorous foundation for the previously largely heuristic field of General AI and embedded agents. There also has been rapid progress in not quite universal but still rather general and practical artificial recurrent neural networks for learning sequence-processing programs, now yielding state-of-the-art results in real world applications. And the computing power per Euro is still growing by a factor of 100–1,000 per decade, greatly increasing the feasibility of neural networks in general, which have started to yield human-competitive results in challenging pattern recognition competitions. Finally, a recent formal theory of fun and creativity identifies basic principles of curious and creative machines, laying foundations for artificial scientists and artists. Here I will briefly review some of the new results of my lab at IDSIA, and speculate about future developments, pointing out that the time intervals between the most notable events in over 40,000 years or \(2^9\) lifetimes of human history have sped up exponentially, apparently converging to zero within the next few decades. Or is this impression just a by-product of the way humans allocate memory space to past events?

Keywords

Reinforcement Learn Proof Search Reinforcement Learn Method Chinese Handwritten Character Traffic Sign Recognition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Balcan, M. F., Beygelzimer, A., & Langford, J. (2009). Agnostic active learning. Journal of Computer and System Sciences, 75(1), 78–89.MathSciNetMATHCrossRefGoogle Scholar
  2. Barto, A. (2013). Intrinsic motivation and reinforcement learning. In G. Baldassarre & M. Mirolli (Eds.), Intrinsically motivated learning in natural and artificial systems. Springer (in press).Google Scholar
  3. Behnke, S. (2003). Hierarchical neural networks for image interpretation, volume 2766 of lecture notes in computer science. Springer.Google Scholar
  4. Bishop, C. M. (2006). Pattern recognition and machine learning. NY: Springer.MATHGoogle Scholar
  5. Bringsjord, S. (2000), ‘A contrarian future for minds and machines’, chronicle of higher education (p. B5). Reprinted in The Education Di-gest, vol. 66(6), pp. 31–33.Google Scholar
  6. Ciresan, D. C., Meier, U., Gambardella, L. M., & Schmidhuber, J. (2010). Deep big simple neural nets for handwritten digit recogntion. Neural Computation, 22(12), 3207–3220.CrossRefGoogle Scholar
  7. Ciresan, D. C., Meier, U., Gambardella, L. M., & Schmidhuber, J. (2011a). Convolutional neural network committees for handwritten character classification. In 11th International Conference on Document Analysis and Recognition (ICDAR), pp 1250–1254.Google Scholar
  8. Ciresan, D. C., Meier, U., Masci, J., Gambardella, L. M. & Schmidhuber, J. (2011b). Flexible, high performance convolutional neural networks for image classification. In International Joint Conference on Artificial Intelligence IJCAI, pp 1237–1242.Google Scholar
  9. Ciresan, D. C., Meier, U., Masci, J., & Schmidhuber, J. (2011c). A committee of neural networks for traffic sign classification. In International Joint Conference on, Neural Networks, pp 1918–1921.Google Scholar
  10. Ciresan, D. C., Meier, U., Masci, J., & Schmidhuber, J. (2012a). Multi-column deep neural network for traffic sign classification. Neural Networks, 32, 333–338.Google Scholar
  11. Ciresan, D. C., Meier, U., & Schmidhuber, J. (2012b). Multi-column deep neural networks for image classification. In IEEE Conference on Computer Vision and Pattern Recognition CVPR 2012, pp 3642–3649.Google Scholar
  12. Ciresan, D. C., Meier, U., & Schmidhuber, J. (2012c). Multi-column deep neural networks for image classification. In IEEE Conference on Computer Vision and Pattern Recognition CVPR 2012. Long preprint arXiv:1202.2745v1 [cs.CV].Google Scholar
  13. Darwin, C. (1997). The descent of man, prometheus, amherst. NY: A reprint edition.Google Scholar
  14. Dayan, P. (2013). Exploration from generalization mediated by multiple controllers. In G. Baldassarre & M. Mirolli (Eds.), Intrinsically motivated learning in natural and artificial systems. Springer (in press).Google Scholar
  15. Fedorov, V. V. (1972). Theory of optimal experiments. NY: Academic.Google Scholar
  16. Fernandez, S., Graves, A., & Schmidhuber, J. (2007). Sequence labelling in structured domains with hierarchical recurrent neural networks. In Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI).Google Scholar
  17. Floridi, L. (2007). A look into the future impact of ICT on our lives. The Information Society, 23(1), 59–64.CrossRefGoogle Scholar
  18. Fukushima, K. (1980). Neocognitron: A self-organizing neural network for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics36(4), 193–202.Google Scholar
  19. Gers, F. A., & Schmidhuber, J. (2001). LSTM recurrent networks learn simple context free and context sensitive languages. IEEE Transactions on Neural Networks, 12(6), 1333–1340.CrossRefGoogle Scholar
  20. Gers, F. A., Schraudolph, N., & Schmidhuber, J. (2002). Learning precise timing with LSTM recurrent networks. Journal of Machine Learning Research, 3, 115–143.MathSciNetGoogle Scholar
  21. Gisslen, L., Luciw, M., Graziano, V., & Schmidhuber, J. (2011). Sequential constant size compressor for reinforcement learning. In Proceedings of Fourth Conference on Artificial General Intelligence (AGI), Google, Mountain View, CA.Google Scholar
  22. Glasmachers, T., Schaul, T., Sun, Y., Wierstra, D. & Schmidhuber, J. (2010). Exponential Natural Evolution Strategies. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO).Google Scholar
  23. Gödel, K. (1931). Über formal unentscheidbare Sätze der Principia Mathematica und verwandter Systeme I. Monatshefte für Mathematik und Physik, 38, 173–198.CrossRefGoogle Scholar
  24. Gomez, F. J., Schmidhuber, J., & Miikkulainen, R. (2008). Efficient non-linear control through neuroevolution. Journal of Machine Learning Research JMLR, 9, 937–965.MathSciNetMATHGoogle Scholar
  25. Graves, A., Fernandez, S., Gomez, F. J., & Schmidhuber, J. (2006). Connectionist temporal classification: Labelling unsegmented sequence data with recurrent neural nets. In ICML ’06: Proceedings of the International Conference on Machine Learning.Google Scholar
  26. Graves, A., Fernandez, S., Liwicki, M., Bunke, H., & Schmidhuber, J. (2008). Unconstrained on-line handwriting recognition with recurrent neural networks. In J. C. Platt, D. Koller, Y. Singer, & S. Roweis (Eds.), Advances in Neural Information Processing Systems 20 (pp. 577–584). Cambridge: MIT Press.Google Scholar
  27. Graves, A., Liwicki, M., Fernandez, S., Bertolami, R., Bunke, H., & Schmidhuber, J. (2009). A novel connectionist system for improved unconstrained handwriting recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(5), 855–868.Google Scholar
  28. Graves, A., & Schmidhuber, J. (2009). Offline handwriting recognition with multidimensional recurrent neural networks. In Advances in Neural Information Processing Systems (p. 21). Cambridge: MIT Press.Google Scholar
  29. Hansen, N., & Ostermeier, A. (2001). Completely derandomized self-adaptation in evolution strategies. Evolutionary Computation, 9(2), 159–195.CrossRefGoogle Scholar
  30. Hart, S., Sen, S., & Grupen, R. (2008). Intrinsically motivated hierarchical manipulation. In Proceedings of the IEEE Conference on Robots and Automation (ICRA). California: Pasadena. Google Scholar
  31. Hochreiter, S., Bengio, Y., Frasconi, P., & Schmidhuber, J. (2001). Gradient flow in recurrent nets: The difficulty of learning long-term dependencies. In S. C. Kremer & J. F. Kolen (Eds.), A Field Guide to Dynamical Recurrent Neural Networks. NJ: IEEE Press.Google Scholar
  32. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.Google Scholar
  33. Holland, J. H. (1975). Adaptation in natural and artificial systems. Ann Arbor: University of Michigan Press.Google Scholar
  34. Hutter, M. (2002). The fastest and shortest algorithm for all well-defined problems. International Journal of Foundations of Computer Science, 13(3), 431–443 (On J. Schmidhuber’s SNF grant 20–61847).Google Scholar
  35. Hutter, M. (2005). Universal Artificial Intelligence: Sequential Decisions Based on Algorithmic Probability. Berlin: Springer (On J. Schmidhuber’s SNF grant 20–61847).Google Scholar
  36. Jaeger, H. (2004). Harnessing nonlinearity: Predicting chaotic systems and saving energy in wireless communication. Science, 304, 78–80.CrossRefGoogle Scholar
  37. Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996). Reinforcement learning: A survey. Journal of AI research, 4, 237–285.Google Scholar
  38. Kolmogorov, A. N. (1965). Three approaches to the quantitative definition of information. Problems of Information Transmission, 1, 1–11.Google Scholar
  39. Koutnik, J., Gomez, F., & Schmidhuber, J. (2010). Evolving neural networks in compressed weight space. In Proceedings of the Conference on Genetic and, Evolutionary Computation (GECCO-10).Google Scholar
  40. Krizhevsky, A. (2009). Learning multiple layers of features from tiny images. Master’s thesis: Computer Science Department, University of Toronto.Google Scholar
  41. Kuipers, B., Beeson, P., Modayil, J., & Provost, J. (2006). Bootstrap learning of foundational representations. Connection Science, 18(2).Google Scholar
  42. Kurzweil, R. (2005). The singularity is near. NY: Wiley Interscience.Google Scholar
  43. LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278–2324.Google Scholar
  44. LeCun, Y., Huang, F.-J., & Bottou, L. (2004). Learning methods for generic object recognition with invariance to pose and lighting. In Proceedings of Computer Vision and Pattern Recognition Conference.Google Scholar
  45. Lenat, D. B. (1983). Theory formation by heuristic search. Machine Learning, vol. 21.Google Scholar
  46. Levin, L. A. (1973). Universal sequential search problems. Problems of Information Transmission, 9(3), 265–266.Google Scholar
  47. Li, M., & Vitányi, P. M. B. (1997). An introduction to kolmogorov complexity and its applications (2nd ed.). NY: Springer.MATHCrossRefGoogle Scholar
  48. Maass, W., Natschläger, T., & Markram, H. (2002). A fresh look at real-time computation in generic recurrent neural circuits. Institute for Theoretical Computer Science, TU Graz : Technical report.Google Scholar
  49. Mitchell, T. (1997). Machine learning. NY: McGraw Hill.MATHGoogle Scholar
  50. Moravec, H. (1999). Robot . NY: Wiley Interscience.Google Scholar
  51. Newell, A., & Simon, H. (1963). GPS, a program that simulates human thought. In E. Feigenbaum & J. Feldman (Eds.), Computers and thought (pp. 279–293). New York: McGraw-Hill.Google Scholar
  52. Oudeyer, P. -Y., Baranes, A., & Kaplan, F. (2013). Intrinsically motivated learning of real world sensorimotor skills with developmental constraints. In G. Baldassarre & M. Mirolli (Eds.), Intrinsically motivated learning in natural and artificial systems. Springer (in press).Google Scholar
  53. Rechenberg, I. (1971). Evolutions strategie–optimierung technischer systeme nach Prinzipien der biologischen Evolution. Dissertation, Published 1973 by Fromman-Holzboog.Google Scholar
  54. Robinson, A. J., & Fallside, F. (1987). The utility driven dynamic error propagation network. Technical Report CUED/F-INFENG/TR.1, Cambridge University Engineering Department.Google Scholar
  55. Rosenbloom, P. S., Laird, J. E., & Newell, A. (1993). The SOAR papers. NY: MIT Press.Google Scholar
  56. Schaul, T., Bayer, J., Wierstra, D., Sun, Y., Felder, M., Sehnke, F., et al. (2010). PyBrain. Journal of Machine Learning Research, 11, 743–746.Google Scholar
  57. Scherer, D., Müller, A., & Behnke, S. (2010). In International Conference on Artificial Neural Networks.Google Scholar
  58. Schmidhuber, J. (1990). Dynamische neuronale Netze und das fundamentale raumzeitliche Lernproblem. Dissertation: Institut für Informatik, Technische Universität München.Google Scholar
  59. Schmidhuber, J. (1991a). Curious model-building control systems. In Proceedings of the International Joint Conference on Neural Networks (vol. 2, pp. 1458–1463). Singapore: IEEE press.Google Scholar
  60. Schmidhuber, J. (1991b). A possibility for implementing curiosity and boredom in model-building neural controllers. In J. A. Meyer & S. W. Wilson (Eds.) Proceedings of the International Conference on Simulation of Adaptive Behavior: From Animals to Animats, pp. 222–227. MIT Press/Bradford Books.Google Scholar
  61. Schmidhuber, J. (1991c). Reinforcement learning in Markovian and non-Markovian environments. In D. S. Lippman, J. E. Moody, & D. S. Touretzky (Eds.), Advances in neural information processing systems 3 (NIPS 3) (pp. 500–506). NY: Morgan Kaufmann.Google Scholar
  62. Schmidhuber, J. (1992a). A fixed size storage \(O(n^3)\) time complexity learning algorithm for fully recurrent continually running networks. Neural Computation, 4(2), 243–248.CrossRefGoogle Scholar
  63. Schmidhuber, J. (1992b). Learning factorial codes by predictability minimization. Neural Computation, 4(6), 863–879.CrossRefGoogle Scholar
  64. Schmidhuber, J. (1997). Discovering neural nets with low Kolmogorov complexity and high generalization capability. Neural Networks, 10(5), 857–873.CrossRefGoogle Scholar
  65. Schmidhuber, J. (1999). Artificial curiosity based on discovering novel algorithmic predictability through coevolution. In P. Angeline, Z. Michalewicz, M. Schoenauer, X. Yao,& Z. Zalzala (Eds.), Congress on evolutionary computation (pp. 1612–1618). Piscataway: IEEE Press.Google Scholar
  66. Schmidhuber, J. (2002a). Hierarchies of generalized Kolmogorov complexities and nonenumerable universal measures computable in the limit. International Journal of Foundations of Computer Science, 13(4), 587–612.MathSciNetMATHCrossRefGoogle Scholar
  67. Schmidhuber, J. (2002). The speed prior: A new simplicity measure yielding near-optimal computable predictions. In J. Kivinen& R. H. Sloan (Eds.), Proceedings of the 15th Annual Conference on Computational Learning Theory (COLT 2002) (pp. 216–228). Lecture Notes in Artificial Intelligence Sydney, Australia: Springer.Google Scholar
  68. Schmidhuber, J. (2003a). Exponential speed-up of computer history’s defining moments. http://www.idsia.ch/juergen/computerhistory.html
  69. Schmidhuber, J. (2003b). The new AI: General & sound & relevant for physics. Technical Report TR IDSIA-04-03, Version 1.0, arXiv:cs.AI/0302012 v1.Google Scholar
  70. Schmidhuber, J. (2004). Optimal ordered problem solver. Machine Learning, 54, 211–254.MATHCrossRefGoogle Scholar
  71. Schmidhuber, J. (2005). Completely self-referential optimal reinforcement learners. In W. Duch, J. Kacprzyk, E. Oja, & S. Zadrozny (Eds.), Artificial neural networks: Biological inspirations–ICANN 2005 (pp. 223–233), LNCS 3697. Springer: Berlin Heidelberg (Plenary talk).Google Scholar
  72. Schmidhuber, J. (2006a). Developmental robotics, optimal artificial curiosity, creativity, music, and the fine arts. Connection Science, 18(2), 173–187.CrossRefGoogle Scholar
  73. Schmidhuber, J. (2006b). Gödel machines: Fully self-referential optimal universal self-improvers. In B. Goertzel& C. Pennachin (Eds.), Artificial general intelligence (pp. 199–226). Heidelberg: Springer (Variant available as arXiv:cs.LO/0309048).Google Scholar
  74. Schmidhuber, J. (2006c). Celebrating 75 years of AI–history and outlook: The next 25 years. In M. Lungarella, F. Iida, J. Bongard,& R. Pfeifer (Eds.), 50 years of artificial intelligence (vol. LNAI 4850, pp. 29–41). Berlin/Heidelberg: Springer (Preprint available as arXiv:0708.4311).Google Scholar
  75. Schmidhuber, J. (2007a). Gödel machines: Fully self-referential optimal universal self-improvers. In B. Goertzel& C. Pennachin (Eds.), Artificial general intelligence (pp. 199–226). Springer Verlag (Variant available as arXiv:cs.LO/0309048).Google Scholar
  76. Schmidhuber, J. (2007b). New millennium AI and the convergence of history. In W. Duch& J. Mandziuk (Eds.), Challenges to computational intelligence (vol. 63, pp. 15–36). Studies in Computational Intelligence, Springer, 2007. Also available as arXiv:cs.AI/0606081.Google Scholar
  77. Schmidhuber, J. (2009). Ultimate cognition à la Gödel. Cognitive Computation, 1(2), 177–193.CrossRefGoogle Scholar
  78. Schmidhuber, J. (2010). Formal theory of creativity, fun, and intrinsic motivation (1990–2010). IEEE Transactions on Autonomous Mental Development, 2(3), 230–247.CrossRefGoogle Scholar
  79. Schmidhuber, J. (2011). PowerPlay: Training an increasingly general problem solver by continually searching for the simplest still unsolvable problem. Technical Report arXiv:1112.5309v1 [cs.AI].Google Scholar
  80. Schmidhuber, J. (2012). Philosophers& futurists, catch up! response to the singularity. Journal of Consciousness Studies, 19(1–2), 173–182.Google Scholar
  81. Schmidhuber, J., Ciresan, D., Meier, U., Masci, J., & Graves, A. (2011). On fast deep nets for AGI vision. In Proceedings of Fourth Conference on Artificial General Intelligence (AGI), Google, Mountain View, CA.Google Scholar
  82. Schmidhuber, J., Eldracher, M., & Foltin, B. (1996). Semilinear predictability minimization produces well-known feature detectors. Neural Computation, 8(4), 773–786.CrossRefGoogle Scholar
  83. Schmidhuber, J., Wierstra, D., Gagliolo, M., & Gomez, F. J. (2007). Training recurrent networks by EVOLINO. Neural Computation, 19(3), 757–779.MATHCrossRefGoogle Scholar
  84. Schmidhuber, J., Zhao, J., & Schraudolph, N. (1997). Reinforcement learning with self-modifying policies. In S. Thrun& L. Pratt (Eds.), Learning to learn (pp. 293–309). NY: Kluwer.Google Scholar
  85. Schraudolph, N. N., Eldracher, M., & Schmidhuber, J. (1999). Processing images by semi-linear predictability minimization. Network: Computation in Neural Systems, 10(2), 133–169.Google Scholar
  86. Schwefel, H. P. (1974). Numerische optimierung von computer-modellen. Dissertation, Published 1977 by Birkhäuser, Basel.Google Scholar
  87. Siegelmann, H. T., & Sontag, E. D. (1991). Turing computability with neural nets. Applied Mathematics Letters, 4(6), 77–80.MathSciNetMATHCrossRefGoogle Scholar
  88. Sims, K. (1994). Evolving virtual creatures. In A. Glassner (Ed.), Proceedings of SIGGRAPH ’94 (Orlando, Florida, July 1994), Computer Graphics Proceedings, Annual Conference (pp. 15–22). ACM SIGGRAPH, ACM Press. ISBN 0-89791-667-0.Google Scholar
  89. Singh, S., Barto, A. G., & Chentanez, N. (2005). Intrinsically motivated reinforcement learning. In Advances in Neural Information Processing Systems 17 (NIPS). Cambridge: MIT Press.Google Scholar
  90. Sloman, A. (2011a, Oct 23). Challenge for vision: Seeing a Toy Crane. Retrieved June 8, 2012, from http://www.cs.bham.ac.uk/research/projects/cosy/photos/crane/
  91. Sloman, A. (2011b, June 8). Meta-morphogenesis and the creativity of evolution. Retrieved 6 June 2012, from http://www.cs.bham.ac.uk/research/projects/cogaff/evo-creativity.pdf
  92. Sloman, A. (2011c, Oct 29). Meta-Morphogenesis and Toddler Theorems: Case Studies. Retrieved 8 June 2012, from http://www.cs.bham.ac.uk/research/projects/cogaff/misc/toddler-theorems.html
  93. Sloman, A. (2011d, Sep 19). Simplicity and Ontologies: The trade-off between simplicity of theories and sophistication of ontologies. Retrieved June 8, 2012, from http://www.cs.bham.ac.uk/research/projects/cogaff/misc/simplicity-ontology.html
  94. Smil, V. (1999). Detonator of the population explosion. Nature, 400, 415.CrossRefGoogle Scholar
  95. Solomonoff, R. J. (1964). A formal theory of inductive inference. Part I. Information and Control, 7, 1–22.MathSciNetMATHCrossRefGoogle Scholar
  96. Stanley, K. O., & Miikkulainen, R. (2002). Evolving neural networks through augmenting topologies. Evolutionary Computation, 10, 99–127.CrossRefGoogle Scholar
  97. Storck, J., Hochreiter, S., & Schmidhuber, J. (1995). Reinforcement driven information acquisition in non-deterministic environments. In Proceedings of the International Conference on Artificial Neural Networks, Paris, vol. 2, pp. 159–164. EC2& Cie, 1995.Google Scholar
  98. Strehl, A., Langford, J., & Kakade, S. (2010). Learning from logged implicit exploration data. Technical, Report arXiv:1003.0120.Google Scholar
  99. Sun, Y., Wierstra, D., Schaul, T., & Schmidhuber, J. (2009a). Efficient natural evolution strategies. In Genetic and Evolutionary Computation Conference.Google Scholar
  100. Sun, Y., Wierstra, D., Schaul, T., & Schmidhuber, J. (2009b). Stochastic search using the natural gradient. In International Conference on Machine Learning (ICML).Google Scholar
  101. Sutskever, I., Martens, J., & Hinton, G. (2011). Generating text with recurrent neural networks. In L. Getoor& T. Scheffer (Eds.), Proceedings of the 28th International Conference on Machine Learning (ICML-11) (pp. 1017–1024). ICML ’11 New York, NY, USA: ACM.Google Scholar
  102. Sutton, R., & Barto, A. (1998). Reinforcement learning: An introduction. Cambridge: MIT Press.Google Scholar
  103. Turing, A. M. (1936). On computable numbers, with an application to the Entscheidungsproblem. Proceedings of the London Mathematical Society, Series, 2(41), 230–267.MathSciNetGoogle Scholar
  104. Utgoff, P. (1986). Shift of bias for inductive concept learning. In R. Michalski, J. Carbonell,& T. Mitchell (Eds.), Machine learning (Vol. 2, pp. 163–190). Los Altos, CA: Morgan Kaufmann.Google Scholar
  105. Vapnik, V. (1995). The nature of statistical learning theory. New York: Springer.MATHCrossRefGoogle Scholar
  106. Vinge, V. (1984). The peace war. Inc. : Bluejay Books.Google Scholar
  107. Vinge, V. (1993). The coming technological singularity. VISION-21 Symposium sponsored by NASA Lewis Research Center, and Whole Earth Review, Winter issue.Google Scholar
  108. Werbos, P. J. (1988). Generalization of backpropagation with application to a recurrent gas market model. Neural Networks, 1.Google Scholar
  109. Wierstra, D., Foerster, A., Peters, J., & Schmidhuber, J. (2010). Recurrent policy gradients. Logic Journal of IGPL,18(2), 620–634.Google Scholar
  110. Wierstra, D., Schaul, T., Peters, J., & Schmidhuber, J. (2008). Natural evolution strategies. In Congress of Evolutionary Computation (CEC 2008).Google Scholar
  111. Williams R. J., & Zipser, D. (1994). Gradient-based learning algorithms for recurrent networks and their computational complexity. In back-propagation: Theory, architectures and applications. Hillsdale, NJ: Erlbaum.Google Scholar
  112. Yao, X. (1993). A review of evolutionary artificial neural networks. International Journal of Intelligent Systems, 4, 203–222.Google Scholar
  113. Yi, S., Gomez, F., & Schmidhuber, J. (2011). Planning to be surprised: Optimal Bayesian exploration in dynamic environments. In Proceedings of Fourth Conference on Artificial General Intelligence (AGI), Google, Mountain View, CA.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.The Swiss AI Lab IDSIAUniversity of Lugano & SUPSIManno-LuganoSwitzerland

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