Gödel Machines: Towards a Technical Justification of Consciousness
暂无分享,去创建一个
[1] Toshio Odanaka,et al. ADAPTIVE CONTROL PROCESSES , 1990 .
[2] Jürgen Schmidhuber,et al. Goedel Machines: Self-Referential Universal Problem Solvers Making Provably Optimal Self-Improvements , 2003, ArXiv.
[3] Jürgen Schmidhuber,et al. Bias-Optimal Incremental Problem Solving , 2002, NIPS.
[4] Chris Mellish,et al. Programming in Prolog (2nd ed.) , 1984 .
[5] Manuel Blum,et al. On Effective Procedures for Speeding Up Algorithms , 1971, JACM.
[6] William F. Clocksin,et al. Programming in Prolog , 1981, Springer Berlin Heidelberg.
[7] SolomonoffR.. Complexity-based induction systems , 2006 .
[8] K. Gödel. Über formal unentscheidbare Sätze der Principia Mathematica und verwandter Systeme I , 1931 .
[9] H. Cantor. Ueber eine Eigenschaft des Inbegriffs aller reellen algebraischen Zahlen. , 1984 .
[10] Ray J. Solomonoff,et al. A Formal Theory of Inductive Inference. Part II , 1964, Inf. Control..
[11] Marcus Hutter,et al. Towards a Universal Theory of Artificial Intelligence Based on Algorithmic Probability and Sequential Decisions , 2000, ECML.
[12] W. Heisenberg. Über den anschaulichen Inhalt der quantentheoretischen Kinematik und Mechanik , 1927 .
[13] R. Penrose,et al. Shadows of the Mind , 1994 .
[14] Jürgen Schmidhuber,et al. Discovering Neural Nets with Low Kolmogorov Complexity and High Generalization Capability , 1997, Neural Networks.
[15] A. Turing. On Computable Numbers, with an Application to the Entscheidungsproblem. , 1937 .
[16] Jürgen Schmidhuber,et al. Hierarchies of Generalized Kolmogorov Complexities and Nonenumerable Universal Measures Computable in the Limit , 2002, Int. J. Found. Comput. Sci..
[17] Leopold Löwenheim. Über Möglichkeiten im Relativkalkül , 1915 .
[18] Konrad Zuse,et al. Rechnender Raum , 1991, Physik und Informatik.
[19] C. Koch,et al. Consciousness and neuroscience. , 1998, Cerebral cortex.
[20] John H. Holland,et al. Properties of the Bucket Brigade , 1985, ICGA.
[21] Wolfgang Banzhaf,et al. Genetic Programming: An Introduction , 1997 .
[22] Jieyu Zhao,et al. Simple Principles of Metalearning , 1996 .
[23] W. Heisenberg. A quantum-theoretical reinterpretation of kinematic and mechanical relations , 1925 .
[24] Jürgen Schmidhuber,et al. A Computer Scientist's View of Life, the Universe, and Everything , 1999, Foundations of Computer Science: Potential - Theory - Cognition.
[25] Marcus Hutter,et al. Self-Optimizing and Pareto-Optimal Policies in General Environments based on Bayes-Mixtures , 2002, COLT.
[26] Andrew W. Moore,et al. Reinforcement Learning: A Survey , 1996, J. Artif. Intell. Res..
[27] Corso Elvezia. Discovering Solutions with Low Kolmogorov Complexity and High Generalization Capability , 1995 .
[28] Manuel Blum,et al. A Machine-Independent Theory of the Complexity of Recursive Functions , 1967, JACM.
[29] Ofi rNw8x'pyzm,et al. The Speed Prior: A New Simplicity Measure Yielding Near-Optimal Computable Predictions , 2002 .
[30] Peter Nordin,et al. Genetic programming - An Introduction: On the Automatic Evolution of Computer Programs and Its Applications , 1998 .
[31] Jürgen Schmidhuber,et al. A ‘Self-Referential’ Weight Matrix , 1993 .
[32] Marcus Hutter,et al. Universal Artificial Intellegence - Sequential Decisions Based on Algorithmic Probability , 2005, Texts in Theoretical Computer Science. An EATCS Series.
[33] K. Popper. All life is problem solving , 1997 .
[34] Jürgen Schmidhuber,et al. Gödel Machines: Fully Self-referential Optimal Universal Self-improvers , 2007, Artificial General Intelligence.
[35] A. Kolmogoroff. Grundbegriffe der Wahrscheinlichkeitsrechnung , 1933 .
[36] Jean-Pierre Bourguignon,et al. Mathematische Annalen , 1893 .
[37] D. Hofstadter,et al. Godel, Escher, Bach: An Eternal Golden Braid , 1979 .
[38] Jürgen Schmidhuber,et al. Reinforcement Learning with Self-Modifying Policies , 1998, Learning to Learn.
[39] Douglas B. Lenat,et al. Theory Formation by Heuristic Search , 1983, Artificial Intelligence.
[40] Melvin Fitting,et al. First-Order Logic and Automated Theorem Proving , 1990, Graduate Texts in Computer Science.
[41] Ray J. Solomonoff,et al. A Formal Theory of Inductive Inference. Part I , 1964, Inf. Control..
[42] Ray J. Solomonoff,et al. Complexity-based induction systems: Comparisons and convergence theorems , 1978, IEEE Trans. Inf. Theory.
[43] Nichael Lynn Cramer,et al. A Representation for the Adaptive Generation of Simple Sequential Programs , 1985, ICGA.
[44] Luc De Raedt,et al. Machine Learning: ECML 2001 , 2001, Lecture Notes in Computer Science.
[45] R. Bellman,et al. V. Adaptive Control Processes , 1964 .
[46] Jürgen Schmidhuber,et al. Algorithmic Theories of Everything , 2000, ArXiv.
[47] William I. Gasarch,et al. Book Review: An introduction to Kolmogorov Complexity and its Applications Second Edition, 1997 by Ming Li and Paul Vitanyi (Springer (Graduate Text Series)) , 1997, SIGACT News.
[48] Arthur L. Samuel,et al. Some Studies in Machine Learning Using the Game of Checkers , 1967, IBM J. Res. Dev..
[49] Sepp Hochreiter,et al. Learning to Learn Using Gradient Descent , 2001, ICANN.
[50] A. Kolmogorov. Three approaches to the quantitative definition of information , 1968 .
[51] Ming Li,et al. An Introduction to Kolmogorov Complexity and Its Applications , 2019, Texts in Computer Science.
[52] H. Rice. Classes of recursively enumerable sets and their decision problems , 1953 .
[53] Juergen Schmidhuber,et al. On learning how to learn learning strategies , 1994 .
[54] Jürgen Schmidhuber,et al. Optimal Ordered Problem Solver , 2002, Machine Learning.
[55] David H. Wolpert,et al. No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..
[56] Kurt Hornik,et al. Artificial Neural Networks — ICANN 2001 , 2001, Lecture Notes in Computer Science.
[57] Jürgen Schmidhuber,et al. Reinforcement Learning in Markovian and Non-Markovian Environments , 1990, NIPS.
[58] D. Wolpert,et al. No Free Lunch Theorems for Search , 1995 .
[59] Richard S. Sutton,et al. Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.
[60] Charles H. Moore,et al. Forth - a language for interactive computing , 1970 .
[61] Marcus Hutter. The Fastest and Shortest Algorithm for all Well-Defined Problems , 2002, Int. J. Found. Comput. Sci..
[62] Helly. Grundbegriffe der Wahrscheinlichkeitsrechnung , 1936 .
[63] Jürgen Schmidhuber,et al. Shifting Inductive Bias with Success-Story Algorithm, Adaptive Levin Search, and Incremental Self-Improvement , 1997, Machine Learning.
[64] Sebastian Thrun,et al. Learning to Learn , 1998, Springer US.
[65] Leonid A. Levin,et al. Randomness Conservation Inequalities; Information and Independence in Mathematical Theories , 1984, Inf. Control..