Introduction to Ray Solomonoff 85th Memorial Conference

[1]  Mehrdad Amirghasemi,et al.  An anthropomorphic method for number sequence problems , 2013, Cognitive Systems Research.

[2]  Alain Finkel,et al.  World Scientific Publishing Company , 2013 .

[3]  José Hernández-Orallo,et al.  On Potential Cognitive Abilities in the Machine Kingdom , 2013, Minds and Machines.

[4]  Kevin B. Korb,et al.  Causal Discovery of Dynamic Bayesian Networks , 2012, Australasian Conference on Artificial Intelligence.

[5]  George Barmpalias,et al.  Universality probability of a prefix-free machine , 2012, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[6]  J. Hernández-Orallo,et al.  IQ tests are not for machines, yet , 2012 .

[7]  Dongmo Zhang,et al.  AI 2012: Advances in Artificial Intelligence , 2012, Lecture Notes in Computer Science.

[8]  D. Dowe,et al.  A novel approach for modeling malaria incidence using complex categorical household data: The minimum message length (MML) method applied to Indonesian data , 2012 .

[9]  Enes Makalic,et al.  Minimum Message Length Analysis of the Behrens-Fisher Problem , 2013, Algorithmic Probability and Friends.

[10]  Jean-Louis Dessalles,et al.  Algorithmic Simplicity and Relevance , 2012, Algorithmic Probability and Friends.

[11]  Eamonn J. Keogh,et al.  Discovering the Intrinsic Cardinality and Dimensionality of Time Series Using MDL , 2011, 2011 IEEE 11th International Conference on Data Mining.

[12]  David Balduzzi,et al.  Falsification and Future Performance , 2011, Algorithmic Probability and Friends.

[13]  Tor Lattimore,et al.  No Free Lunch versus Occam's Razor in Supervised Learning , 2011, Algorithmic Probability and Friends.

[14]  José Hernández-Orallo,et al.  Evaluating a Reinforcement Learning Algorithm with a General Intelligence Test , 2011, CAEPIA.

[15]  Marcus Hutter,et al.  Principles of Solomonoff Induction and AIXI , 2011, Algorithmic Probability and Friends.

[16]  Geoffrey I. Webb,et al.  Learning by extrapolation from marginal to full-multivariate probability distributions: decreasingly naive Bayesian classification , 2011, Machine Learning.

[17]  Shane Legg,et al.  An Approximation of the Universal Intelligence Measure , 2011, Algorithmic Probability and Friends.

[18]  José Hernández-Orallo,et al.  Compression and Intelligence: Social Environments and Communication , 2011, AGI.

[19]  José Hernández-Orallo,et al.  Comparing Humans and AI Agents , 2011, AGI.

[20]  José Hernández-Orallo,et al.  On More Realistic Environment Distributions for Defining, Evaluating and Developing Intelligence , 2011, AGI.

[21]  Eray Özkural,et al.  Diverse Consequences of Algorithmic Probability , 2011, Algorithmic Probability and Friends.

[22]  Vasant Honavar,et al.  Abstraction Super-Structuring Normal Forms: Towards a Theory of Structural Induction , 2011, Algorithmic Probability and Friends.

[23]  Hayato Takahashi.  Algorithmic Analogies to Kamae-Weiss Theorem on Normal Numbers , 2011, Algorithmic Probability and Friends.

[24]  Joel Veness,et al.  A Monte-Carlo AIXI Approximation , 2009, J. Artif. Intell. Res..

[25]  Grace Solomonoff.  Ray Solomonoff and the New Probability , 2011, Algorithmic Probability and Friends.

[26]  Ray J. Solomonoff,et al.  On the Application of Algorithmic Probability to Autoregressive Models , 2011, Algorithmic Probability and Friends.

[27]  Anang Hudaya Muhamad Amin,et al.  Developing Machine Intelligence within P2P Networks Using a Distributed Associative Memory , 2011, Algorithmic Probability and Friends.

[28]  P. Allen King.  Design of a Conscious Machine , 2011, Algorithmic Probability and Friends.

[29]  Douglas I. Campbell,et al.  The Semimeasure Property of Algorithmic Probability - "Feature" or "Bug"? , 2011, Algorithmic Probability and Friends.

[30]  Leonid A. Levin,et al.  Universal Heuristics: How Do Humans Solve "Unsolvable" Problems? , 2011, Algorithmic Probability and Friends.

[31]  Kenji Araki,et al.  Limiting Context by Using the Web to Minimize Conceptual Jump Size , 2011, Algorithmic Probability and Friends.

[32]  J. Storrs Hall.  Further Reflections on the Timescale of AI , 2011, Algorithmic Probability and Friends.

[33]  Alex Solomonoff,et al.  Locating a Discontinuity in a Piecewise-Smooth Periodic Function Using Bayes Estimation , 2011, Algorithmic Probability and Friends.

[34]  Daniel F. Schmidt,et al.  Minimum Message Length Order Selection and Parameter Estimation of Moving Average Models , 2011, Algorithmic Probability and Friends.

[35]  Joe Suzuki.  MDL/Bayesian Criteria Based on Universal Coding/Measure , 2011, Algorithmic Probability and Friends.

[36]  Ricardo Luis de Azevedo da Rocha,et al.  Learning in the Limit: A Mutational and Adaptive Approach , 2011, Algorithmic Probability and Friends.

[37]  Ming Li.  Partial Match Distance - In Memoriam Ray Solomonoff 1926-2009 , 2011, Algorithmic Probability and Friends.

[38]  Eamonn J. Keogh,et al.  Discovering the Intrinsic Cardinality and Dimensionality of Time Series Using MDL , 2011, 2011 IEEE 11th International Conference on Data Mining.

[39]  Steve Petersen,et al.  Toward an Algorithmic Metaphysics , 2011, Algorithmic Probability and Friends.

[40]  Nir Fresco,et al.  A Critical Survey of Some Competing Accounts of Concrete Digital Computation , 2011, Algorithmic Probability and Friends.

[41]  Rusins Freivalds,et al.  Algorithmic Information Theory and Computational Complexity , 2011, Algorithmic Probability and Friends.

[42]  T. Mark Ellison,et al.  Categorisation as Topographic Mapping between Uncorrelated Spaces , 2011, Algorithmic Probability and Friends.

[43]  Norbert Jankowski,et al.  Complexity Measures for Meta-learning and Their Optimality , 2011, Algorithmic Probability and Friends.

[44]  Kristiaan Pelckmans,et al.  An Adaptive Compression Algorithm in a Deterministic World , 2011, Algorithmic Probability and Friends.

[45]  Enes Makalic,et al.  MMLD Inference of Multilayer Perceptrons , 2011, Algorithmic Probability and Friends.

[46]  Kenshi Miyabe,et al.  An Optimal Superfarthingale and Its Convergence over a Computable Topological Space , 2011, Algorithmic Probability and Friends.

[47]  Jerry Swan,et al.  A Syntactic Approach to Prediction , 2011, Algorithmic Probability and Friends.

[48]  José Hernández-Orallo,et al.  Measuring universal intelligence: Towards an anytime intelligence test , 2010, Artif. Intell..

[49]  Matthias Dehmer,et al.  Information Theory and Statistical Learning , 2010 .

[50]  Ray J. Solomonoff,et al.  Algorithmic Probability, Heuristic Programming and AGI , 2010, AGI 2010.

[51]  D. Dowe,et al.  A conceptual model for integrating physical geography research and coastal wetland management, with an Australian example , 2010 .

[52]  D. Dowe,et al.  management, with an Australian example A conceptual model for integrating physical geography research and coastal wetland , 2010 .

[53]  David L. Dowe,et al.  MML, hybrid Bayesian network graphical models, statistical consistency, invarianc , 2010 .

[54]  David L. Dowe,et al.  Enhancing MML Clustering Using Context Data with Climate Applications , 2009, Australasian Conference on Artificial Intelligence.

[55]  Ray J. Solomonofi.  Machine Learning | Past and Future , 2009 .

[56]  Xiaodong Li,et al.  AI 2009: Advances in Artificial Intelligence, 22nd Australasian Joint Conference, Melbourne, Australia, December 1-4, 2009. Proceedings , 2009, Australasian Conference on Artificial Intelligence.

[57]  John McCarthy,et al.  Algorithmic Probability — Its Discovery — Its Properties and Application to Strong AI , 2009 .

[58]  Ray J. Solomonoff,et al.  Algorithmic Probability|Theory and Applications , 2009 .

[59]  David L. Dowe,et al.  Minimum Message Length and Statistically Consistent Invariant (Objective?) Bayesian Probabilistic Inference—From (Medical) “Evidence” , 2008 .

[60]  M. H. Brennan.  Data Processing in the Early Cosmic Ray Experiments in Sydney , 2008, Comput. J..

[61]  David L. Dowe,et al.  Foreword re C. S. Wallace , 2008, Comput. J..

[62]  Ray J. Solomonoff,et al.  Three Kinds of Probabilistic Induction: Universal Distributions and Convergence Theorems , 2008, Comput. J..

[63]  Ray J. Solomonoff,et al.  The probability of "undefined" (non-converging) output in generating the universal probability distribution , 2008, Inf. Process. Lett..

[64]  David L. Dowe,et al.  A computer program capable of passing I.Q. tests , 2008 .

[65]  Shane Legg,et al.  Universal Intelligence: A Definition of Machine Intelligence , 2007, Minds and Machines.

[66]  David L. Dowe,et al.  Bayes not Bust! Why Simplicity is no Problem for Bayesians1 , 2007, The British Journal for the Philosophy of Science.

[67]  David L. Dowe,et al.  Minimum Message Length Clustering of Spatially-Correlated Data with Varying Inter-Class Penalties , 2007, 6th IEEE/ACIS International Conference on Computer and Information Science (ICIS 2007).

[68]  David L. Dowe.  Discussion on hedging predictions in machine learning by A Gammerman and V Vovk , 2007 .

[69]  David L. Dowe,et al.  Decision Forests with Oblique Decision Trees , 2006, MICAI.

[70]  W. Bruce Croft,et al.  LDA-based document models for ad-hoc retrieval , 2006, SIGIR.

[71]  R. Solomonoff.  Does Algorithmic Probability Solve the Problem of Induction , 2006 .

[72]  Kai Ming Ting,et al.  Model-based clustering of sequential data , 2006 .

[73]  Jorma Rissanen,et al.  Information and Complexity in Statistical Modeling , 2006, ITW.

[74]  Alexander Gelbukh,et al.  MICAI 2006: Advances in Artificial Intelligence, 5th Mexican International Conference on Artificial Intelligence, Apizaco, Mexico, November 13-17, 2006, Proceedings , 2006, MICAI.

[75]  C. S. Wallace,et al.  Statistical and Inductive Inference by Minimum Message Length (Information Science and Statistics) , 2005 .

[76]  David L. Dowe,et al.  Minimum message length and generalized Bayesian nets with asymmetric languages , 2005 .

[77]  Leigh J. Fitzgibbon,et al.  Minimum message length autoregressive model order selection , 2004, International Conference on Intelligent Sensing and Information Processing, 2004. Proceedings of.

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

[79]  C. S. Wallace,et al.  Coding Decision Trees , 1993, Machine Learning.

[80]  R. Solomonoff.  Comments on Dr. S. Watanabe's paper , 1962, Synthese.

[81]  R. Solomonoff.  Progress In Incremental Machine Learning , 2003 .

[82]  David L. Dowe,et al.  General Bayesian networks and asymmetric languages , 2003 .

[83]  R. Solomonoff.  The Kolmogorov Lecture* The Universal Distribution and Machine Learning , 2003, Comput. J..

[84]  Kevin B. Korb,et al.  Bayesian Information Reward , 2002, Australian Joint Conference on Artificial Intelligence.

[85]  Lloyd Allison,et al.  Univariate Polynomial Inference by Monte Carlo Message Length Approximation , 2002, ICML.

[86]  Marcus Hutter.  New Error Bounds for Solomonoff Prediction , 2001, J. Comput. Syst. Sci..

[87]  C. E. SHANNON,et al.  A mathematical theory of communication , 1948, MOCO.

[88]  David L. Dowe,et al.  Message Length as an Effective Ockham's Razor in Decision Tree Induction , 2001, International Conference on Artificial Intelligence and Statistics.

[89]  R. Solomonoff.  A PRELIMINARY REPORT ON A GENERAL THEORY OF INDUCTIVE INFERENCE , 2001 .

[90]  José Hernández-Orallo,et al.  Beyond the Turing Test , 2000, J. Log. Lang. Inf..

[91]  David L. Dowe,et al.  MML clustering of multi-state, Poisson, von Mises circular and Gaussian distributions , 2000, Stat. Comput..

[92]  Lloyd Allison,et al.  MML Markov classification of sequential data , 1999, Stat. Comput..

[93]  Matthew V. Mahoney,et al.  Text Compression as a Test for Artificial Intelligence , 1999, AAAI/IAAI.

[94]  Ray J. Solomonoff,et al.  Two Kinds of Probabilistic Induction , 1999, Comput. J..

[95]  David L. Dowe,et al.  Minimum Message Length and Kolmogorov Complexity , 1999, Comput. J..

[96]  Robert A. Wilson,et al.  Book Reviews: The MIT Encyclopedia of the Cognitive Sciences , 2000, CL.

[97]  David L. Dowe,et al.  Refinements of MDL and MML Coding , 1999, Comput. J..

[98]  David L. Dowe,et al.  Point Estimation Using the Kullback-Leibler Loss Function and MML , 1998, PAKDD.

[99]  David L. Dowe,et al.  Single Factor Analysis in MML Mixture Modelling , 1998, PAKDD.

[100]  David L. Dowe,et al.  A Non-Behavioural, Computational Extension to the Turing Test , 1998 .

[101]  Xindong Wu,et al.  Research and Development in Knowledge Discovery and Data Mining , 1998, Lecture Notes in Computer Science.

[102]  C. S. Wallace,et al.  Intrinsic Classification of Spatially Correlated Data , 1998, Comput. J..

[103]  Ray J. Solomonoff,et al.  The Discovery of Algorithmic Probability , 1997, J. Comput. Syst. Sci..

[104]  David L. Dowe,et al.  A computational extension to the Turing test , 1997 .

[105]  C. S. Wallace,et al.  MML mixture modelling of multi-state, Poisson, von Mises circular and Gaussian distributions , 1997 .

[106]  Wolfgang J. Paul,et al.  Autonomous theory building systems , 1995, Ann. Oper. Res..

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

[108]  Andrew R. Barron,et al.  Minimum complexity density estimation , 1991, IEEE Trans. Inf. Theory.

[109]  C. S. Wallace,et al.  Classification by Minimum-Message-Length Inference , 1991, ICCI.

[110]  L. N. Kanal,et al.  Uncertainty in Artificial Intelligence 5 , 1990 .

[111]  Ray J. Solomonofi,et al.  A SYSTEM FOR INCREMENTAL LEARNING BASED ON ALGORITHMIC PROBABILITY , 1989 .

[112]  Mark S. Boddy,et al.  An Analysis of Time-Dependent Planning , 1988, AAAI.

[113]  N. Sharkey,et al.  Advances in cognitive science , 1988 .

[114]  H. Akaike.  Factor analysis and AIC , 1987 .

[115]  C. S. Wallace,et al.  Estimation and Inference by Compact Coding , 1987 .

[116]  E. B. Andersen,et al.  Information Science and Statistics , 1986 .

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

[118]  R. J. Solomonoff,et al.  The time scale of artificial intelligence: Reflections on social effects , 1985 .

[119]  Glen G. Langdon,et al.  An Introduction to Arithmetic Coding , 1984, IBM J. Res. Dev..

[120]  G. Chaitin.  Gödel's theorem and information , 1982 .

[121]  Glen G. Langdon,et al.  A simple general binary source code , 1982, IEEE Trans. Inf. Theory.

[122]  Glen G. Langdon,et al.  Arithmetic Coding , 1979 .

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

[124]  Ray J. Solomonoff,et al.  Complexity-based induction systems: Comparisons and convergence theorems , 1978, IEEE Trans. Inf. Theory.

[125]  G. Schwarz.  Estimating the Dimension of a Model , 1978 .

[126]  Jorma Rissanen,et al.  Generalized Kraft Inequality and Arithmetic Coding , 1976, IBM J. Res. Dev..

[127]  R. J. Solomonoff,et al.  Inductive inference theory: a unified approach to problems in pattern recognition and artificial intelligence , 1975, IJCAI 1975.

[128]  G. Chaitin.  Randomness and Mathematical Proof , 1975 .

[129]  C. S. Wallace,et al.  An Information Measure for Single Link Classification , 1975, Comput. J..

[130]  H. Akaike,et al.  Information Theory and an Extension of the Maximum Likelihood Principle , 1973 .

[131]  C. S. Wallace,et al.  Occupancy of a Rectangular Array , 1973, Comput. J..

[132]  C. S. Wallace,et al.  An Information Measure for Hierarchic Classification , 1973, Comput. J..

[133]  James J. Horning,et al.  A Procedure for Grammatical Inference , 1971, IFIP Congress.

[134]  H. Akaike.  Statistical predictor identification , 1970 .

[135]  Chris S. Wallace,et al.  A Program for Numerical Classification , 1970, Comput. J..

[136]  Gregory J. Chaitin,et al.  On the Simplicity and Speed of Programs for Computing Infinite Sets of Natural Numbers , 1969, J. ACM.

[137]  D M Boulton,et al.  The information content of a multistate distribution. , 1969, Journal of theoretical biology.

[138]  Andrei N. Kolmogorov,et al.  Logical basis for information theory and probability theory , 1968, IEEE Trans. Inf. Theory.

[139]  C. S. Wallace,et al.  An Information Measure for Classification , 1968, Comput. J..

[140]  R. J. Solomonoff.  The search for artificial intelligence , 1968 .

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

[142]  Ray J. Solomonofi.  INDUCTIVE INFERENCE RESEARCH STATUS, SPRING 1967 , 1967 .

[143]  R. J. Solomonoff,et al.  Some recent work in artificial intelligence , 1966 .

[144]  David Lewis,et al.  Scriven on human unpredictability , 1966 .

[145]  Gregory J. Chaitin,et al.  On the Length of Programs for Computing Finite Binary Sequences , 1966, JACM.

[146]  S. T. Butler,et al.  Atoms to Andromeda , 1966 .

[147]  I. J. Good,et al.  Speculations Concerning the First Ultraintelligent Machine , 1965, Adv. Comput..

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

[149]  Thomas G. Evans,et al.  A heuristic program to solve geometric-analogy problems , 1964, AFIPS '64 (Spring).

[150]  R. J. Solomonoff.  A CODING METHOD FOR INDUCTIVE INFERENCE , 1961 .

[151]  Ray J. Solomonoff,et al.  A new method for discovering the grammars of phrase structure languages , 1959, IFIP Congress.

[152]  C. S. WALLACE,et al.  Air Showers of Size Greater than 105 Particles: (I) Core Location and Shower Size Determination , 1958, Nature.

[153]  S. Ulam.  John von Neumann 1903-1957 , 1958 .

[154]  R. Solomonoff.  An exact method for the computation of the connectivity of random nets , 1952 .

[155]  A. Rapoport,et al.  Connectivity of random nets , 1951 .

[156]  A. M. Turing,et al.  Computing Machinery and Intelligence , 1950, The Philosophy of Artificial Intelligence.

[157]  H. Jeffreys.  An invariant form for the prior probability in estimation problems , 1946, Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences.

[158]  A. Turing.  On Computable Numbers, with an Application to the Entscheidungsproblem. , 1937 .