The DOI matches its Crossref record; 71 of the 71 references it cites were found in a registry or an index.
Gödel Machines: Fully Self-referential Optimal Universal Self-improvers
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Citations
Cites
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 25,268
No free lunch theorems for optimization
Wolpert, D. H., & Macready, W. G. (1997) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.585893 · Cited by 14,237
Advances in Neural Information Processing Systems 14
(2002) · The MIT Press eBooks · doi:10.7551/mitpress/1120.001.0001 · Cited by 9,187
Reinforcement Learning: A Survey
Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996) · Journal of Artificial Intelligence Research · doi:10.1613/jair.301 · Cited by 8,972
On computable numbers, with an application to the entscheidungsproblem
Turing, A. M. (1937) · Proceedings of the London Mathematical Society · doi:10.1112/plms/s2-42.1.230 · Cited by 8,283
Lecture Notes in Artificial Intelligence
Brézillon, P., & Bouquet, P. (1999) · Cited by 7,436
Some Studies in Machine Learning Using the Game of Checkers
Samuel, A. L. (1959) · IBM Journal of Research and Development · doi:10.1147/rd.33.0210 · Cited by 4,443
�ber den anschaulichen Inhalt der quantentheoretischen Kinematik und Mechanik
Heisenberg, W. (1927) · Zeitschrift f�r Physik · doi:10.1007/bf01397280 · Cited by 4,278
An Introduction to Kolmogorov Complexity and Its Applications
Li, M., & Vitanyi, P. M. B. (2019) · Texts in computer science · doi:10.1007/978-3-030-11298-1 · Cited by 3,769
Adaptive Control Processes: A Guided Tour
Bellman, R. E. (1961) · doi:10.1515/9781400874668 · Cited by 2,802
Über formal unentscheidbare Sätze der Principia Mathematica und verwandter Systeme I
Gödel, K. (1931) · Monatshefte für Mathematik und Physik · doi:10.1007/bf01700692 · Cited by 2,395 · cited 5×
“The fundamental limitations are closely related to those first identified by Gödel’s celebrated paper on self-referential formulae [11].”
BackgroundInfluentialBarto, A. (1998) · IFAC Proceedings Volumes · doi:10.1016/s1474-6670(17)38315-5 · Cited by 2,172
Three approaches to the quantitative definition of information *
Kolmogorov, A. N. (1968) · International Journal of Computer Mathematics · doi:10.1080/00207166808803030 · Cited by 2,107
Über formal unentscheidbare Sätze der Principia Mathematica und verwandter Systeme I
Gödel, K. (2006) · Monatshefte für Mathematik · doi:10.1007/s00605-006-0423-7 · Cited by 2,096
Godel, Escher, Bach: An Eternal Golden Braid.
Lieberson, J., & Hofstadter, D. R. (1980) · The Journal of Philosophy · doi:10.2307/2025598 · Cited by 2,076
Clocksin, W. F., & Mellish, C. (1984) · Springer eBooks · doi:10.1007/978-3-642-55481-0 · Cited by 2,047
Zhang, B., Li, C., & Lin, N. (2020) · De Gruyter eBooks · doi:10.1515/9783110595567-011 · Cited by 1,948
An Introduction to Kolmogorov Complexity and Its Applications
Li, M., & Vitanyi, P. M. B. (1997) · Springer eBooks · doi:10.1007/978-1-4757-2606-0 · Cited by 1,792
A formal theory of inductive inference. Part II
Solomonoff, R. J. (1964) · Information and Control · doi:10.1016/s0019-9958(64)90131-7 · Cited by 1,758
Grundbegriffe der Wahrscheinlichkeitsrechnung
Kolmogoroff, A. (1933) · Springer eBooks · doi:10.1007/978-3-642-49888-6 · Cited by 1,742
�ber quantentheoretische Umdeutung kinematischer und mechanischer Beziehungen.
Heisenberg, W. K. (1925) · The European Physical Journal A · doi:10.1007/bf01328377 · Cited by 1,482
Clocksin, W., & Mellish, C. (1987) · Springer Berlin Heidelberg · doi:10.1007/978-3-642-97005-4 · Cited by 1,454
Some studies in machine learning using the game of checkers
Samuel, A. L. (2000) · IBM Journal of Research and Development · doi:10.1147/rd.441.0206 · Cited by 1,439
Thrun, S., & Pratt, L. (1998) · Springer eBooks · doi:10.1007/978-1-4615-5529-2 · Cited by 1,291
A formal theory of inductive inference. Part I
Solomonoff, R. J. (1964) · Information and Control · doi:10.1016/s0019-9958(64)90223-2 · Cited by 1,192
Genetic Programming: An Introduction
Banzhaf, W., Keller, R. E., & Nordin, P. C. (1997) · Cited by 1,071
Bellman, R. (1964) · IEEE Transactions on Circuit Theory · doi:10.1109/tct.1964.1082221 · Cited by 1,003
First-Order Logic and Automated Theorem Proving
Fitting, M. (1996) · Springer eBooks · doi:10.1007/978-1-4612-2360-3 · Cited by 950
Problems of Information Transmission
(2013) · Problems of Information Transmission · doi:10.1134/11122.1608-3253 · Cited by 930
No Free Lunch Theorems for SearchPreprint
Wolpert, D. H., & Macready, W. G. (1995) · RePEc: Research Papers in Economics · Cited by 927
A Theory of Program Size Formally Identical to Information Theory
Chaitin, G. J. (1975) · Journal of the ACM · doi:10.1145/321892.321894 · Cited by 831
A Machine-Independent Theory of the Complexity of Recursive Functions
Blum, M. (1967) · Journal of the ACM · doi:10.1145/321386.321395 · Cited by 831
Banzhaf, W., Fogarty, T. C., Schoenauer, M., & Poli, R. (1998) · Lecture notes in computer science · doi:10.1007/bfb0055923 · Cited by 825
Consciousness and neuroscience
Crick, F. (1998) · Cerebral Cortex · doi:10.1093/cercor/8.2.97 · Cited by 783
A Representation for the Adaptive Generation of Simple Sequential Programs
Cramer, N. L. (1985) · international conference on Genetic algorithms · Cited by 626
Learning to Learn Using Gradient Descent
Hochreiter, S., Younger, A. S., & Conwell, P. R. (2001) · Lecture notes in computer science · doi:10.1007/3-540-44668-0_13 · Cited by 589
Hooker, J., & Publishing, N. J. (2012) · Journal of Business Ethics Education · doi:10.5840/jbee201291 · Cited by 529
Über Möglichkeiten im Relativkalkül
Löwenheim, L. (1915) · Mathematische Annalen · doi:10.1007/bf01458217 · Cited by 398
Complexity-based induction systems: Comparisons and convergence theorems
Solomonoff, R. J. (1978) · IEEE Transactions on Information Theory · doi:10.1109/tit.1978.1055913 · Cited by 379
“All life is problem solving.”
Schweizer, P., & Zuerich, M. G. (2015) · Cited by 369
Popper, K. (2013) · Routledge eBooks · doi:10.4324/9780203431900 · Cited by 281
Randomness conservation inequalities; information and independence in mathematical theories
Levin, L. A. (1984) · Information and Control · doi:10.1016/s0019-9958(84)80060-1 · Cited by 246
Schmidhuber, J., Zhao, J., & Wiering, M. (1997) · Machine Learning · doi:10.1023/a:1007383707642 · Cited by 171
Self-citationOptimal Ordered Problem Solver
Schmidhuber, J. (2004) · Machine Learning · doi:10.1023/b:mach.0000015880.99707.b2 · Cited by 162
Self-citationDiscovering Neural Nets with Low Kolmogorov Complexity and High Generalization Capability
Schmidhuber, J. (1997) · Neural Networks · doi:10.1016/s0893-6080(96)00127-x · Cited by 148
Self-citationUeber eine Eigenschaft des Inbegriffes aller reellen algebraischen Zahlen
Cantor, H. G. (1984) · Teubner-Archiv zur Mathematik · doi:10.1007/978-3-7091-9516-1_2 · Cited by 148
Zuse, K. (1969) · Vieweg+Teubner eBooks · doi:10.1007/978-3-663-02723-2 · Cited by 136
Properties of the Bucket Brigade
Holland, J. H. (1985) · international conference on Genetic algorithms · Cited by 122
A computer scientist's view of life, the universe, and everything
Schmidhuber, J. (1997) · Lecture notes in computer science · doi:10.1007/bfb0052088 · Cited by 119
Self-citationThe Speed Prior: A New Simplicity Measure Yielding Near-Optimal Computable Predictions
Schmidhuber, J. (2002) · Lecture notes in computer science · doi:10.1007/3-540-45435-7_15 · Cited by 111
Self-citationTheory formation by heuristic search
Lenat, D. B. (1983) · Artificial Intelligence · doi:10.1016/s0004-3702(83)80004-6 · Cited by 101
Algorithmic Theories of EverythingPreprint
Schmidhuber, J. (2000) · arXiv (Cornell University) · doi:10.48550/arxiv.quant-ph/0011122 · Cited by 99
Self-citationSchmidhuber, J. (2002) · International Journal of Foundations of Computer Science · doi:10.1142/s0129054102001291 · Cited by 97
Self-citationReinforcement Learning in Markovian and Non-Markovian Environments
Schmidhuber, J. (1990) · Cited by 89
Self-citationSimple Principles of Metalearning
Schmidhuber, J., Zhao, J., & Wiering, M. (1996) · doi:10.71910/supsi.7463 · Cited by 69
Self-citationOn Effective Procedures for Speeding Up Algorithms
Blum, M. (1971) · Journal of the ACM · doi:10.1145/321637.321648 · Cited by 65
Reinforcement Learning with Self-Modifying Policies
Schmidhuber, J., Zhao, J., & Schraudolph, N. N. (1998) · Springer eBooks · doi:10.1007/978-1-4615-5529-2_12 · Cited by 63
Self-citationTHE FASTEST AND SHORTEST ALGORITHM FOR ALL WELL-DEFINED PROBLEMS
Hütter, M. (2002) · International Journal of Foundations of Computer Science · doi:10.1142/s0129054102001199 · Cited by 63
Adaptive Agents and Multi-Agent Systems: Adaptation and Multi-Agent Learning
Alonso, E., Kudenko⋆, D., & Kazakov, D. (2003) · doi:10.1007/3-540-44826-8 · Cited by 56
A ‘Self-Referential’ Weight Matrix
Schmidhuber, J. (1993) · Springer eBooks · doi:10.1007/978-1-4471-2063-6_107 · Cited by 56
Self-citationFoundations of computer science : potential--theory--cognition
Brauer, W., Freksa, C., Jantzen, M., et al. (1997) · Springer eBooks · Cited by 55
Hütter, M. (2001) · Lecture notes in computer science · doi:10.1007/3-540-44795-4_20 · Cited by 54
Discovering Solutions with Low Kolmogorov Complexity and High Generalization Capability
Schmidhuber, J. (1995) · Elsevier eBooks · doi:10.1016/b978-1-55860-377-6.50067-0 · Cited by 43
Self-citationSelf-Optimizing and Pareto-Optimal Policies in General Environments Based on Bayes-Mixtures
Hütter, M. (2002) · Lecture notes in computer science · doi:10.1007/3-540-45435-7_25 · Cited by 39
The New AI: General & Sound & Relevant for Physics
Schmidhuber, J. (2007) · Cognitive technologies · doi:10.1007/978-3-540-68677-4_6 · Cited by 30
Self-citationCompletely Self-referential Optimal Reinforcement Learners
Schmidhuber, J. (2005) · Lecture notes in computer science · doi:10.1007/11550907_36 · Cited by 28
Self-citationSchmidhuber, J. (2003) · arXiv · doi:10.48550/arxiv.cs/0309048 · Cited by 24
Self-citationBias-Optimal Incremental Problem Solving
Schmidhuber, J. (2002) · Cited by 20
Self-citationGödel Machines: Towards a Technical Justification of Consciousness
Schmidhuber, J. (2005) · Lecture notes in computer science · doi:10.1007/978-3-540-32274-0_1 · Cited by 14
Self-citationThe New AI: General & Sound & Relevant for PhysicsPreprint
Schmidhuber, J. (2003) · arXiv (Cornell University) · Cited by 5
Self-citation
Flagged first, then by citations
Research works printed but not linked: 5
- Levin LA (1973) Universal sequential search problems. Problems of Information Transmission, 9(3):265–266.
- Penrose R (1994) Shadows of the mind. Oxford University Press, Oxford.
- Schmidhuber J (1987) Evolutionary principles in self-referential learning. Diploma thesis, Institut für Informatik, Technische Universität München.
- Skolem T (1919) Logisch-kombinatorische Untersuchungen über Erfüllbarkeit oder Beweisbarkeit mathematischer Sätze nebst einem Theorem über dichte Mengen. Skrifter utgit av Videnskapsselskapet in Kristiania, I, Mat.-Nat. Kl., N4:1–36.
- Solomonoff R (2003) Progress in incremental machine learning-Preliminary Report for NIPS 2002 Workshop on Universal Learners and Optimal Search
Cited by
Sorted by citations
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570
Self-citationFormal theory of creativity, fun, and intrinsic motivation (1990–2010)
Schmidhuber, J. (2010) · IEEE Transactions on Autonomous Mental Development · doi:10.1109/tamd.2010.2056368 · Cited by 747
Self-citationArtificial General Intelligence: Concept, State of the Art, and Future Prospects
Goertzel, B. (2014) · Journal of Artificial General Intelligence · doi:10.2478/jagi-2014-0001 · Cited by 554
Artificial Intelligence as a Positive and Negative Factor in Global Risk
Yudkowsky, E. (2008) · Global Catastrophic Risks · doi:10.1093/oso/9780198570509.003.0021 · Cited by 431
Evolved Policy GradientsPreprint
Houthooft, R., Chen, R. Y., Isola, P., Stadie, B. C., Wolski, F., Ho, J., & Abbeel, P. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.04821 · Cited by 168
Metadata
- Authors
- Schmidhuber, J.
- Affiliations
- Dalle Molle Institute for Artificial Intelligence Research (CH)
- Corresponding author
- Schmidhuber, Jürgen
- ORCID
- Schmidhuber, Jürgen
- Venue
- Cognitive Technologies
- Publisher
- Springer Berlin Heidelberg
- Published
- 2007
- Pages
- 199-226
- ISSN
- 1611-2482 (ISSN-L)
- OpenAlex
- W158468099
- Semantic Scholar
- 402a5f749c99c75c98a626539553c2e4b5939c29
- Cited by
- 110
- References
- 71
- Citations by year
- 2026: 16 · 2025: 2 · 2024: 1 · 2023: 5 · 2022: 5 · 2021: 6 · 2020: 7 · 2019: 4 · 2018: 8 · 2017: 4 · 2016: 4 · 2015: 5 · 2014: 6 · 2013: 3 · 2012: 9
- FWCI
- 13.91
- Type
- book chapter
- Language
- en
- Open access
- closed
- Licence
- Locations
- Cognitive technologies (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computability, Logic, AI Algorithms
- Physical Sciences › Computer Science › Software › Logic, programming, and type systems
- Physical Sciences › Computer Science › Artificial Intelligence › Evolutionary Algorithms and Applications
- Keywords
- self-improving systems, proof search, optimal complexity, utility functions, local optima avoidance
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
