next up previous
Next: About this document ... Up: Optimal Ordered Problem Solver Previous: Initial User-Defined Programs: Examples

Bibliography

1
C. W. Anderson.
Learning and Problem Solving with Multilayer Connectionist Systems.
PhD thesis, University of Massachusetts, Dept. of Comp. and Inf. Sci., 1986.

2
W. Banzhaf, P. Nordin, R. E. Keller, and F. D. Francone.
Genetic Programming - An Introduction.
Morgan Kaufmann Publishers, San Francisco, CA, USA, 1998.

3
E. B. Baum and I. Durdanovic.
Toward a model of mind as an economy of agents.
Machine Learning, 35(2):155-185, 1999.

4
C. H. Bennett.
The thermodynamics of computation, a review.
International Journal of Theoretical Physics, 21(12):905-940, 1982.

5
C. M. Bishop.
Neural networks for pattern recognition.
Oxford University Press, 1995.

6
H. J. Bremermann.
Minimum energy requirements of information transfer and computing.
International Journal of Theoretical Physics, 21:203-217, 1982.

7
G.J. Chaitin.
A theory of program size formally identical to information theory.
Journal of the ACM, 22:329-340, 1975.

8
N. L. Cramer.
A representation for the adaptive generation of simple sequential programs.
In J.J. Grefenstette, editor, Proceedings of an International Conference on Genetic Algorithms and Their Applications, Carnegie-Mellon University, July 24-26, 1985, Hillsdale NJ, 1985. Lawrence Erlbaum Associates.

9
Y. Deville and K. K. Lau.
Logic program synthesis.
Journal of Logic Programming, 19(20):321-350, 1994.

10
D. Dickmanns, J. Schmidhuber, and A. Winklhofer.
Der genetische Algorithmus: Eine Implementierung in Prolog. Fortgeschrittenenpraktikum, Institut für Informatik, Lehrstuhl Prof. Radig, Technische Universität München, 1987.

11
M. Dorigo, G. Di Caro, and L. M. Gambardella.
Ant algorithms for discrete optimization.
Artificial Life, 5(2):137-172, 1999.

12
E. F. Fredkin and T. Toffoli.
Conservative logic.
International Journal of Theoretical Physics, 21(3/4):219-253, 1982.

13
L. M. Gambardella and M. Dorigo.
An ant colony system hybridized with a new local search for the sequential ordering problem.
INFORMS Journal on Computing, 12(3):237-255, 2000.

14
K. Gödel.
Über formal unentscheidbare Sätze der Principia Mathematica und verwandter Systeme I.
Monatshefte für Mathematik und Physik, 38:173-198, 1931.

15
C. C. Green.
Application of theorem proving to problem solving.
In D. E. Walker and L. M. Norton, editors, Proceedings of the 1st International Joint Conference on Artificial Intelligence, IJCAI, pages 219-240. Morgan Kaufmann, 1969.

16
S. Hochreiter, A. S. Younger, and P. R. Conwell.
Learning to learn using gradient descent.
In Lecture Notes on Comp. Sci. 2130, Proc. Intl. Conf. on Artificial Neural Networks (ICANN-2001), pages 87-94. Springer: Berlin, Heidelberg, 2001.

17
J. H. Holland.
Adaptation in Natural and Artificial Systems.
University of Michigan Press, Ann Arbor, 1975.

18
J. H. Holland.
Properties of the bucket brigade.
In Proceedings of an International Conference on Genetic Algorithms. Lawrence Erlbaum, Hillsdale, NJ, 1985.

19
M. Hutter.
Towards a universal theory of artificial intelligence based on algorithmic probability and sequential decisions.
Proceedings of the 12$^{th}$ European Conference on Machine Learning (ECML-2001), pages 226-238, 2001.
(On J. Schmidhuber's SNF grant 20-61847).

20
M. Hutter.
The fastest and shortest algorithm for all well-defined problems.
International Journal of Foundations of Computer Science, 13(3):431-443, 2002.
(On J. Schmidhuber's SNF grant 20-61847).

21
M. Hutter.
Self-optimizing and Pareto-optimal policies in general environments based on Bayes-mixtures.
In J. Kivinen and R. H. Sloan, editors, Proceedings of the 15th Annual Conference on Computational Learning Theory (COLT 2002), Lecture Notes in Artificial Intelligence, pages 364-379, Sydney, Australia, 2002. Springer.
(On J. Schmidhuber's SNF grant 20-61847).

22
M. I. Jordan and D. E. Rumelhart.
Supervised learning with a distal teacher.
Technical Report Occasional Paper #40, Center for Cog. Sci., Massachusetts Institute of Technology, 1990.

23
P. J. Koopman Jr.
Stack Computers: the new wave.
http://www-2.cs.cmu.edu/~ koopman/stack_computers/index.html, 1989.

24
L.P. Kaelbling, M.L. Littman, and A.W. Moore.
Reinforcement learning: a survey.
Journal of AI research, 4:237-285, 1996.

25
J. Koehler, B. Nebel, J. Hoffmann, and Y. Dimopoulos.
Extending planning graphs to an ADL subset.
In S. Steel, editor, Proceedings of the 4th European Conference on Planning, volume 1348 of LNAI, pages 273-285. Springer, 1997.

26
A.N. Kolmogorov.
Three approaches to the quantitative definition of information.
Problems of Information Transmission, 1:1-11, 1965.

27
I. Kwee, M. Hutter, and J. Schmidhuber.
Market-based reinforcement learning in partially observable worlds.
Proceedings of the International Conference on Artificial Neural Networks (ICANN-2001), (IDSIA-10-01, cs.AI/0105025), 2001.

28
P. Langley.
Learning to search: from weak methods to domain-specific heuristics.
Cognitive Science, 9:217-260, 1985.

29
D. Lenat.
Theory formation by heuristic search.
Machine Learning, 21, 1983.

30
L. A. Levin.
Universal sequential search problems.
Problems of Information Transmission, 9(3):265-266, 1973.

31
L. A. Levin.
Laws of information (nongrowth) and aspects of the foundation of probability theory.
Problems of Information Transmission, 10(3):206-210, 1974.

32
L. A. Levin.
Randomness conservation inequalities: Information and independence in mathematical theories.
Information and Control, 61:15-37, 1984.

33
M. Li and P. M. B. Vitányi.
An Introduction to Kolmogorov Complexity and its Applications (2nd edition).
Springer, 1997.

34
S. Lloyd.
Ultimate physical limits to computation.
Nature, 406:1047-1054, 2000.

35
T. Mitchell.
Machine Learning.
McGraw Hill, 1997.

36
C. H. Moore and G. C. Leach.
FORTH - a language for interactive computing, 1970.
http://www.ultratechnology.com.

37
A. Newell and H. Simon.
GPS, a program that simulates human thought.
In E. Feigenbaum and J. Feldman, editors, Computers and Thought, pages 279-293. McGraw-Hill, New York, 1963.

38
Nguyen and B. Widrow.
The truck backer-upper: An example of self learning in neural networks.
In Proceedings of the International Joint Conference on Neural Networks, pages 357-363. IEEE Press, 1989.

39
J. R. Olsson.
Inductive functional programming using incremental program transformation.
Artificial Intelligence, 74(1):55-83, 1995.

40
I. Rechenberg.
Evolutionsstrategie - Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. Dissertation, 1971.
Published 1973 by Fromman-Holzboog.

41
P. S. Rosenbloom, J. E. Laird, and A. Newell.
The SOAR Papers.
MIT Press, 1993.

42
D. E. Rumelhart, G. E. Hinton, and R. J. Williams.
Learning internal representations by error propagation.
In D. E. Rumelhart and J. L. McClelland, editors, Parallel Distributed Processing, volume 1, pages 318-362. MIT Press, 1986.

43
S. Russell and P. Norvig.
Artificial Intelligence: A Modern Approach.
Prentice Hall, Englewood Cliffs, NJ, 1994.

44
R. P. Saustowicz and J. Schmidhuber.
Probabilistic incremental program evolution.
Evolutionary Computation, 5(2):123-141, 1997.

45
R. P. Saustowicz and J. Schmidhuber.
Evolving structured programs with hierarchical instructions and skip nodes.
In Jude Shavlik, editor, Machine Learning: Proceedings of the Fifeteenth International Conference (ICML'98), pages 488-496. Morgan Kaufmann Publishers, San Francisco, 1998.

46
R. P. Saustowicz, M. A. Wiering, and J. Schmidhuber.
Learning team strategies: Soccer case studies.
Machine Learning, 33(2/3):263-282, 1998.

47
J. Schmidhuber.
Evolutionary principles in self-referential learning. Diploma thesis, Institut für Informatik, Technische Universität München, 1987.

48
J. Schmidhuber.
Reinforcement learning in Markovian and non-Markovian environments.
In D. S. Lippman, J. E. Moody, and D. S. Touretzky, editors, Advances in Neural Information Processing Systems 3, pages 500-506. Morgan Kaufmann, 1991.

49
J. Schmidhuber.
An introspective network that can learn to run its own weight change algorithm.
In Proc. of the Intl. Conf. on Artificial Neural Networks, Brighton, pages 191-195. IEE, 1993.

50
J. Schmidhuber.
A self-referential weight matrix.
In Proceedings of the International Conference on Artificial Neural Networks, Amsterdam, pages 446-451. Springer, 1993.

51
J. Schmidhuber.
On learning how to learn learning strategies.
Technical Report FKI-198-94, Fakultät für Informatik, Technische Universität München, 1994.
See [65,63].

52
J. Schmidhuber.
Discovering solutions with low Kolmogorov complexity and high generalization capability.
In A. Prieditis and S. Russell, editors, Machine Learning: Proceedings of the Twelfth International Conference, pages 488-496. Morgan Kaufmann Publishers, San Francisco, CA, 1995.

53
J. Schmidhuber.
Discovering neural nets with low Kolmogorov complexity and high generalization capability.
Neural Networks, 10(5):857-873, 1997.

54
J. Schmidhuber.
Algorithmic theories of everything.
Technical Report IDSIA-20-00, quant-ph/0011122, IDSIA, Manno (Lugano), Switzerland, 2000.
Sections 1-5: see [57]; Section 6: see [58].

55
J. Schmidhuber.
Sequential decision making based on direct search.
In R. Sun and C. L. Giles, editors, Sequence Learning: Paradigms, Algorithms, and Applications. Springer, 2001.
Lecture Notes on AI 1828.

56
J. Schmidhuber.
Exploring the predictable.
In A. Ghosh and S. Tsuitsui, editors, Advances in Evolutionary Computing, pages 579-612. Springer, 2002.

57
J. Schmidhuber.
Hierarchies of generalized Kolmogorov complexities and nonenumerable universal measures computable in the limit.
International Journal of Foundations of Computer Science, 13(4):587-612, 2002.

58
J. Schmidhuber.
The Speed Prior: a new simplicity measure yielding near-optimal computable predictions.
In J. Kivinen and R. H. Sloan, editors, Proceedings of the 15th Annual Conference on Computational Learning Theory (COLT 2002), Lecture Notes in Artificial Intelligence, pages 216-228. Springer, Sydney, Australia, 2002.

59
J. Schmidhuber.
Gödel machines: self-referential universal problem solvers making provably optimal self-improvements.
Technical Report IDSIA-19-03, arXiv:cs.LO/0309048 v2, IDSIA, Manno-Lugano, Switzerland, October 2003.

60
J. Schmidhuber.
The new AI: General & sound & relevant for physics.
Technical Report TR IDSIA-04-03, Version 1.0, cs.AI/0302012 v1, February 2003.

61
J. Schmidhuber.
The new AI: General & sound & relevant for physics.
In B. Goertzel and C. Pennachin, editors, Real AI: New Approaches to Artificial General Intelligence. Plenum Press, New York, 2003.
To appear. Also available as TR IDSIA-04-03, cs.AI/0302012.

62
J. Schmidhuber.
Towards solving the grand problem of AI.
In P. Quaresma, A. Dourado, E. Costa, and J. F. Costa, editors, Soft Computing and complex systems, pages 77-97. Centro Internacional de Mathematica, Coimbra, Portugal, 2003.
Based on [60].

63
J. Schmidhuber, J. Zhao, and N. Schraudolph.
Reinforcement learning with self-modifying policies.
In S. Thrun and L. Pratt, editors, Learning to learn, pages 293-309. Kluwer, 1997.

64
J. Schmidhuber, J. Zhao, and M. Wiering.
Simple principles of metalearning.
Technical Report IDSIA-69-96, IDSIA, 1996.
See [65,63].

65
J. Schmidhuber, J. Zhao, and M. Wiering.
Shifting inductive bias with success-story algorithm, adaptive Levin search, and incremental self-improvement.
Machine Learning, 28:105-130, 1997.

66
J. Schmidhuber, V. Zhumatiy, and M. Gagliolo.
Bias-optimal incremental learning of control sequences for virtual robots.
In Proc. 8th Conference on Intelligent Autonomous Systems IAS-8, Amsterdam, NL, 2004.

67
H. P. Schwefel.
Numerische Optimierung von Computer-Modellen. Dissertation, 1974.
Published 1977 by Birkhäuser, Basel.

68
R.J. Solomonoff.
A formal theory of inductive inference. Part I.
Information and Control, 7:1-22, 1964.

69
R.J. Solomonoff.
An application of algorithmic probability to problems in artificial intelligence.
In L. N. Kanal and J. F. Lemmer, editors, Uncertainty in Artificial Intelligence, pages 473-491. Elsevier Science Publishers, 1986.

70
R.J. Solomonoff.
A system for incremental learning based on algorithmic probability.
In Proceedings of the Sixth Israeli Conference on Artificial Intelligence, Computer Vision and Pattern Recognition, pages 515-527. Tel Aviv, Israel, 1989.

71
M. Tsukamoto.
Program stacking technique.
Information Processing in Japan (Information Processing Society of Japan), 17(1):114-120, 1977.

72
A. M. Turing.
On computable numbers, with an application to the Entscheidungsproblem.
Proceedings of the London Mathematical Society, Series 2, 41:230-267, 1936.

73
S. Ulam.
Random processes and transformations.
In Proceedings of the International Congress on Mathematics, volume 2, pages 264-275, 1950.

74
P. Utgoff.
Shift of bias for inductive concept learning.
In R. Michalski, J. Carbonell, and T. Mitchell, editors, Machine Learning, volume 2, pages 163-190. Morgan Kaufmann, Los Altos, CA, 1986.

75
V. Vapnik.
Principles of risk minimization for learning theory.
In D. S. Lippman, J. E. Moody, and D. S. Touretzky, editors, Advances in Neural Information Processing Systems 4, pages 831-838. Morgan Kaufmann, 1992.

76
J. von Neumann.
Theory of Self-Reproducing Automata.
University of Illionois Press, Champain, IL, 1966.

77
R. J. Waldinger and R. C. T. Lee.
PROW: a step toward automatic program writing.
In D. E. Walker and L. M. Norton, editors, Proceedings of the 1st International Joint Conference on Artificial Intelligence, IJCAI, pages 241-252. Morgan Kaufmann, 1969.

78
P. J. Werbos.
Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences.
PhD thesis, Harvard University, 1974.

79
P. J. Werbos.
Learning how the world works: Specifications for predictive networks in robots and brains.
In Proceedings of IEEE International Conference on Systems, Man and Cybernetics, N.Y., 1987.

80
M.A. Wiering and J. Schmidhuber.
Solving POMDPs with Levin search and EIRA.
In L. Saitta, editor, Machine Learning: Proceedings of the Thirteenth International Conference, pages 534-542. Morgan Kaufmann Publishers, San Francisco, CA, 1996.

81
D. H. Wolpert and W. G. Macready.
No free lunch theorems for search.
IEEE Transactions on Evolutionary Computation, 1, 1997.

82
K. Zuse.
Rechnender Raum.
Friedrich Vieweg & Sohn, Braunschweig, 1969.
English translation: Calculating Space, MIT Technical Translation AZT-70-164-GEMIT, Massachusetts Institute of Technology (Proj. MAC), Cambridge, Mass. 02139, Feb. 1970.


Juergen Schmidhuber 2004-04-15

Back to OOPS main page