The New AI is General & Mathematically Rigorous

[1]  Allen Newell,et al.  GPS, a program that simulates human thought , 1995 .

[2]  L. E. J. Brouwer,et al.  Over de Grondslagen der Wiskunde , 2009 .

[3]  Sepp Hochreiter,et al.  Learning to Learn Using Gradient Descent , 2001, ICANN.

[4]  Jürgen Schmidhuber,et al.  Artificial Scientists & Artists Based on the Formal Theory of Creativity , 2010, AGI 2010.

[5]  P. Strevens.  Iii , 1985 .

[6]  Marcus Hutter.  Simulation Algorithms for Computational Systems Biology , 2017, Texts in Theoretical Computer Science. An EATCS Series.

[7]  E. Mark Gold,et al.  Limiting recursion , 1965, Journal of Symbolic Logic.

[8]  Teuvo Kohonen,et al.  Self-Organization and Associative Memory , 1988 .

[9]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[10]  Andrew W. Moore,et al.  Reinforcement Learning: A Survey , 1996, J. Artif. Intell. Res..

[11]  D. C. Cooper,et al.  Theory of Recursive Functions and Effective Computability , 1969, The Mathematical Gazette.

[12]  Corso Elvezia.  Discovering Solutions with Low Kolmogorov Complexity and High Generalization Capability , 1995 .

[13]  Gary James Jason,et al.  The Logic of Scientific Discovery , 1988 .

[14]  S. Hochreiter,et al.  REINFORCEMENT DRIVEN INFORMATION ACQUISITION IN NONDETERMINISTIC ENVIRONMENTS , 1995 .

[15]  J. Schmidhuber.  Science as By-Products of Search for Novel Patterns , or Data Compressible in Unknown Yet Learnable Ways , 2009 .

[16]  G. Chaitin.  A Theory of Program Size Formally Identical to Information Theory , 1975, JACM.

[17]  Jürgen Schmidhuber,et al.  A possibility for implementing curiosity and boredom in model-building neural controllers , 1991 .

[18]  Jürgen Schmidhuber,et al.  Simple Algorithmic Principles of Discovery, Subjective Beauty, Selective Attention, Curiosity & Creativity , 2007, Discovery Science.

[19]  Jürgen Schmidhuber,et al.  Developmental robotics, optimal artificial curiosity, creativity, music, and the fine arts , 2006, Connect. Sci..

[20]  田中 正,et al.  SUPERSTRING THEORY , 1989, The Lancet.

[21]  Luca Maria Gambardella,et al.  Ant Algorithms for Discrete Optimization , 1999, Artificial Life.

[22]  Jürgen Schmidhuber,et al.  Gödel Machines: Fully Self-referential Optimal Universal Self-improvers , 2007, Artificial General Intelligence.

[23]  Jürgen Schmidhuber,et al.  Discovering Neural Nets with Low Kolmogorov Complexity and High Generalization Capability , 1997, Neural Networks.

[24]  Ray J. Solomonoff,et al.  A Formal Theory of Inductive Inference. Part I , 1964, Inf. Control..

[25]  Konrad Zuse,et al.  Rechnender Raum , 1991, Physik und Informatik.

[26]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[27]  Neri Merhav,et al.  Universal Prediction , 1998, IEEE Trans. Inf. Theory.

[28]  Jürgen Schmidhuber,et al.  Driven by Compression Progress , 2008, KES.

[29]  Jürgen Schmidhuber,et al.  Optimal Ordered Problem Solver , 2002, Machine Learning.

[30]  P. Werbos,et al.  Beyond Regression : "New Tools for Prediction and Analysis in the Behavioral Sciences , 1974 .

[31]  Michael Barr,et al.  The Emperor's New Mind , 1989 .

[32]  H. Everett.  "Relative State" Formulation of Quantum Mechanics , 1957 .

[33]  Verified software based remote attestation for user applications and the OS kernel , 2011 .

[34]  R. Solomonoff.  A SYSTEM FOR INCREMENTAL LEARNING BASED ON ALGORITHMIC PROBABILITY , 1989 .

[35]  T. Erber,et al.  Randomness in quantum mechanics—nature's ultimate cryptogram? , 1985, Nature.

[36]  Hilary Putnam,et al.  Trial and error predicates and the solution to a problem of Mostowski , 1965, Journal of Symbolic Logic.

[37]  Jürgen Schmidhuber,et al.  Simple algorithmic theory of subjective beauty, novelty, surprise, interestingness, attention, curiosity, creativity, art, science, music, jokes (特集 高次機能の学習と創発--脳・ロボット・人間研究における新たな展開) , 2009 .

[38]  Jürgen Schmidhuber,et al.  Dynamische neuronale Netze und das fundamentale raumzeitliche Lernproblem , 1990 .

[39]  Jürgen Schmidhuber,et al.  Shifting Inductive Bias with Success-Story Algorithm, Adaptive Levin Search, and Incremental Self-Improvement , 1997, Machine Learning.

[40]  Péter Gács,et al.  On the relation between descriptional complexity and algorithmic probability , 1981, 22nd Annual Symposium on Foundations of Computer Science (sfcs 1981).

[41]  Jürgen Schmidhuber,et al.  Low-Complexity Art , 2017 .

[42]  Jürgen Schmidhuber,et al.  New Millennium AI and the Convergence of History: Update of 2012 , 2012 .

[43]  Jürgen Schmidhuber,et al.  Ultimate Cognition à la Gödel , 2009, Cognitive Computation.

[44]  Charles H. Moore,et al.  Forth - a language for interactive computing , 1970 .

[45]  Jürgen Schmidhuber.  Discovering Solutions with Low Kolmogorov Complexity and High Generalization Capability , 1995, ICML.

[46]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[47]  A. Kolmogorov.  Three approaches to the quantitative definition of information , 1968 .

[48]  Jürgen Schmidhuber,et al.  A Computer Scientist's View of Life, the Universe, and Everything , 1999, Foundations of Computer Science: Potential - Theory - Cognition.

[49]  Jürgen Schmidhuber,et al.  The New AI: General & Sound & Relevant for Physics , 2003, Artificial General Intelligence.

[50]  Ofi rNw8x'pyzm,et al.  The Speed Prior: A New Simplicity Measure Yielding Near-Optimal Computable Predictions , 2002 .

[51]  Jean-Pierre Bourguignon,et al.  Mathematische Annalen , 1893 .

[52]  Jürgen Schmidhuber,et al.  Curious model-building control systems , 1991, [Proceedings] 1991 IEEE International Joint Conference on Neural Networks.

[53]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[54]  Jürgen Schmidhuber,et al.  Hierarchies of Generalized Kolmogorov Complexities and Nonenumerable Universal Measures Computable in the Limit , 2002, Int. J. Found. Comput. Sci..

[55]  Jürgen Schmidhuber.  2006: Celebrating 75 Years of AI - History and Outlook: The Next 25 Years , 2006, 50 Years of Artificial Intelligence.

[56]  Jürgen Schmidhuber,et al.  Completely Self-referential Optimal Reinforcement Learners , 2005, ICANN.

[57]  Ming Li,et al.  An Introduction to Kolmogorov Complexity and Its Applications , 2019, Texts in Computer Science.

[58]  A. Whitaker.  The Fabric of Reality , 2001 .

[59]  M. Mahoney,et al.  History of Mathematics , 1924, Nature.

[60]  Marcus Hutter,et al.  Algorithmic Information Theory , 1977, IBM J. Res. Dev..

[61]  Thomas G. Dietterich.  What is machine learning? , 2020, Archives of Disease in Childhood.

[62]  Jürgen Schmidhuber,et al.  Bias-Optimal Incremental Problem Solving , 2002, NIPS.

[63]  A. Turing.  On computable numbers, with an application to the Entscheidungsproblem , 1937, Proc. London Math. Soc..

[64]  Ptter.  ON THE RELATION BETWEEN DESCRIPTIONAL COMPLEXITY AND ALGORITHMIC PROBABILITY * , 2001 .

[65]  Marcus Hutter.  The Fastest and Shortest Algorithm for all Well-Defined Problems , 2002, Int. J. Found. Comput. Sci..

[66]  Nils J. Nilsson,et al.  Principles of Artificial Intelligence , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[67]  Jürgen Schmidhuber,et al.  Solving POMDPs with Levin Search and EIRA , 1996, ICML.

[68]  J. Bell.  On the Problem of Hidden Variables in Quantum Mechanics , 1966 .

[69]  Helly.  Grundbegriffe der Wahrscheinlichkeitsrechnung , 1936 .

[70]  David P. DiVincenzo,et al.  Quantum information and computation , 2000, Nature.

[71]  C. Schmidhuber.  Strings from Logic , 2000, hep-th/0011065.

[72]  Jürgen Schmidhuber,et al.  Driven by Compression Progress: A Simple Principle Explains Essential Aspects of Subjective Beauty, Novelty, Surprise, Interestingness, Attention, Curiosity, Creativity, Art, Science, Music, Jokes , 2008, ABiALS.

[73]  Rolf Pfeifer,et al.  Understanding intelligence , 2020, Inequality by Design.

[74]  G. Hooft.  Quantum gravity as a dissipative deterministic system , 1999, gr-qc/9903084.

[75]  Ingo Rechenberg,et al.  Evolutionsstrategie : Optimierung technischer Systeme nach Prinzipien der biologischen Evolution , 1973 .

[76]  J. Rissanen,et al.  Modeling By Shortest Data Description* , 1978, Autom..

[77]  Rodney A. Brooks,et al.  Intelligence Without Reason , 1991, IJCAI.

[78]  John von Neumann,et al.  Theory Of Self Reproducing Automata , 1967 .

[79]  T. Toffoli,et al.  Conservative logic , 2002, Collision-Based Computing.

[80]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[81]  J. Urgen Schmidhuber.  A Computer Scientist's View of Life, the Universe, and Everything , 1997 .

[82]  Jürgen Schmidhuber,et al.  Exploring the predictable , 2003 .

[83]  Ray J. Solomonoff,et al.  The Application of Algorithmic Probability to Problems in Artificial Intelligence , 1985, UAI.

[84]  Jürgen Schmidhuber,et al.  An on-line algorithm for dynamic reinforcement learning and planning in reactive environments , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[85]  Paul E. Utgoff,et al.  Shift of bias for inductive concept learning , 1984 .