Next: About this document ...
Up: A GENERAL METHOD FOR
Previous: ACKNOWLEDGEMENTS
-
- 1
-
L. Adleman.
Time, space, and randomness.
Technical Report MIT/LCS/79/TM-131, Laboratory for Computer Science,
MIT, 1979.
- 2
-
A. G. Barto.
Connectionist approaches for control.
Technical Report COINS 89-89, University of Massachusetts, Amherst MA
01003, 1989.
- 3
-
D. A. Berry and B. Fristedt.
Bandit Problems: Sequential Allocation of Experiments.
Chapman and Hall, London, 1985.
- 4
-
M. Boddy and T. L. Dean.
Deliberation scheduling for problem solving in time-constrained
environments.
Artificial Intelligence, 67:245-285, 1994.
- 5
-
G.J. Chaitin.
On the length of programs for computing finite binary sequences:
statistical considerations.
Journal of the ACM, 16:145-159, 1969.
- 6
-
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, Hillsdale NJ, 1985.
Lawrence Erlbaum Associates.
- 7
-
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.
- 8
-
T. G. Dietterich.
Limitations of inductive learning.
In Proceedings of the Sixth International Workshop on Machine
Learning, Ithaca, NY, pages 124-128. San Francisco, CA: Morgan Kaufmann,
1989.
- 9
-
J. C. Gittins.
Multi-armed Bandit Allocation Indices.
Wiley-Interscience series in systems and optimization. Wiley,
Chichester, NY, 1989.
- 10
-
R. Greiner.
PALO: A probabilistic hill-climbing algorithm.
Artificial Intelligence, 83(2), 1996.
- 11
-
S. Heil.
Universelle Suche und inkrementelles Lernen, diploma thesis, 1995.
Fakultät für Informatik, Lehrstuhl Prof. Brauer, Technische
Universität München.
- 12
-
F. Hoffmeister and T. Bäck.
Genetic algorithms and evolution strategies: Similarities and
differences.
In R. Männer and H. P. Schwefel, editors, Proc. of 1st
International Conference on Parallel Problem Solving from Nature, Berlin.
Springer, 1991.
- 13
-
J. H. Holland.
Adaptation in Natural and Artificial Systems.
University of Michigan Press, Ann Arbor, 1975.
- 14
-
A.N. Kolmogorov.
Three approaches to the quantitative definition of information.
Problems of Information Transmission, 1:1-11, 1965.
- 15
-
J. R. Koza.
Genetic Programming II - Automatic Discovery of Reusable
Programs.
MIT Press, 1994.
- 16
-
P. R. Kumar and P. Varaiya.
Stochastic Systems: Estimation, Identification, and Adaptive
Control.
Prentice Hall, 1986.
- 17
-
D. Lenat.
Theory formation by heuristic search.
Machine Learning, 21, 1983.
- 18
-
L. A. Levin.
Universal sequential search problems.
Problems of Information Transmission, 9(3):265-266, 1973.
- 19
-
L. A. Levin.
Laws of information (nongrowth) and aspects of the foundation of
probability theory.
Problems of Information Transmission, 10(3):206-210, 1974.
- 20
-
L. A. Levin.
Randomness conservation inequalities: Information and independence in
mathematical theories.
Information and Control, 61:15-37, 1984.
- 21
-
M. Li and P. M. B. Vitányi.
An Introduction to Kolmogorov Complexity and its
Applications.
Springer, 1993.
- 22
-
W. Paul and R. J. Solomonoff.
Autonomous theory building systems, 1991.
Manuscript, revised 1994.
- 23
-
I. Rechenberg.
Evolutionsstrategie - Optimierung technischer Systeme nach
Prinzipien der biologischen Evolution. Dissertation, 1971.
Published 1973 by Fromman-Holzboog.
- 24
-
M. B. Ring.
Continual Learning in Reinforcement Environments.
PhD thesis, University of Texas at Austin, Austin, Texas 78712,
August 1994.
- 25
-
S. Russell and E. Wefald.
Principles of Metareasoning.
Artificial Intelligence, 49:361-395, 1991.
- 26
-
C. Schaffer.
Overfitting avoidance as bias.
Machine Learning, 10:153-178, 1993.
- 27
-
J. Schmidhuber.
Evolutionary principles in self-referential learning, or on learning
how to learn: the meta-meta-... hook. Institut für Informatik, Technische
Universität München, 1987.
- 28
-
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. San
Mateo, CA: Morgan Kaufmann, 1991.
- 29
-
J. Schmidhuber.
A neural network that embeds its own meta-levels.
In Proc. of the International Conference on Neural Networks '93,
San Francisco. IEEE, 1993.
- 30
-
J. Schmidhuber.
A self-referential weight matrix.
In Proceedings of the International Conference on Artificial
Neural Networks, Amsterdam, pages 446-451. Springer, 1993.
- 31
-
J. Schmidhuber.
Discovering problem solutions with low Kolmogorov complexity and
high generalization capability.
Technical Report FKI-194-94, Fakultät für Informatik,
Technische Universität München, 1994.
Short version in A. Prieditis and S. Russell, eds., Machine Learning:
Proceedings of the Twelfth International Conference, Morgan Kaufmann
Publishers, pages 488-496, San Francisco, CA, 1995.
- 32
-
J. Schmidhuber.
On learning how to learn learning strategies.
Technical Report FKI-198-94, Fakultät für Informatik,
Technische Universität München, 1994.
Revised 1995.
- 33
-
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.
- 34
-
J. Schmidhuber.
Environment-independent reinforcement acceleration.
Technical Report Note IDSIA-59-95, IDSIA, June 1995.
Invited talk at Hongkong University of Science and Technology.
- 35
-
J. Schmidhuber.
A general method for multi-agent learning in unrestricted
environments.
In Adaptation, Co-evolution and Learning in Multiagent Systems,
Technical Report SS-96-01, pages 84-87. American Association for Artificial
Intelligence, Menlo Park, Calif., 1996.
- 36
-
J. Schmidhuber.
Realistic multi-agent reinforcement learning.
In G. Weiss, editor, Learning in Distributed Artificial
Intelligence Systems. Working Notes of the 1996 ECAI Workshop. 1996.
- 37
-
J. Schmidhuber.
A computer scientist's view of life, the universe, and everything.
In C. Freksa, M. Jantzen, and R. Valk, editors, Foundations of
Computer Science: Theory, Cognition, Applications, volume 1337, pages
201-208. Lecture Notes in Computer Science, Springer, Berlin, 1997.
- 38
-
J. Schmidhuber.
Discovering neural nets with low Kolmogorov complexity and high
generalization capability.
Neural Networks, 10(5):857-873, 1997.
- 39
-
J. Schmidhuber.
What's interesting?
Technical Report IDSIA-35-97, IDSIA, 1997.
- 40
-
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.
- 41
-
J. Schmidhuber, J. Zhao, and M. Wiering.
Simple principles of metalearning.
Technical Report IDSIA-69-96, IDSIA, 1996.
- 42
-
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.
- 43
-
H. P. Schwefel.
Numerische Optimierung von Computer-Modellen. Dissertation, 1974.
Published 1977 by Birkhäuser, Basel.
- 44
-
C. E. Shannon.
A mathematical theory of communication (parts I and II).
Bell System Technical Journal, XXVII:379-423, 1948.
- 45
-
R.J. Solomonoff.
A formal theory of inductive inference. Part I.
Information and Control, 7:1-22, 1964.
- 46
-
R.J. Solomonoff.
A system for incremental learning based on algorithmic probability.
In E. P. D. Pednault, editor, The Theory and Application of
Minimal-Length Encoding (Preprint of Symposium papers of AAAI 1990 Spring
Symposium), 1990.
- 47
-
R. S. Sutton.
Integrated modeling and control based on reinforcement learning and
dynamic programming.
In D. S. Lippman, J. E. Moody, and D. S. Touretzky, editors, Advances in Neural Information Processing Systems 3, pages 471-478. San
Mateo, CA: Morgan Kaufmann, 1991.
- 48
-
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.
- 49
-
C. J. C. H. Watkins and P. Dayan.
Q-learning.
Machine Learning, 8:279-292, 1992.
- 50
-
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.
- 51
-
R. J. Williams.
Simple statistical gradient-following algorithms for connectionist
reinforcement learning.
Machine Learning, 8:229-256, 1992.
- 52
-
D. H. Wolpert.
The lack of a priori distinctions between learning algorithms.
Neural Computation, 8(7):1341-1390, 1996.
- 53
-
J. Zhao and J. Schmidhuber.
Incremental self-improvement for life-time multi-agent reinforcement
learning.
In Pattie Maes, Maja Mataric, Jean-Arcady Meyer, Jordan Pollack, and
Stewart W. Wilson, editors, From Animals to Animats 4: Proceedings of
the Fourth International Conference on Simulation of Adaptive Behavior,
Cambridge, MA, pages 516-525. MIT Press, Bradford Books, 1996.
Juergen Schmidhuber
2003-02-19