Algorithmic Probability and Friends. Bayesian Prediction and Artificial Intelligence Papers from the Ray Solomonoff 85th Memorial Conference, Melbourne, VIC, Australia, November 30 - December 2, 2011
This proceedings of the Ray Solomonoff 85th memorial conference, presents 35 papers on universal Bayesian prediction and artificial intelligence (machine learning). A tribute to Solomonoff's work, which influences modern data mining, econometrics and more.
Saved in:
| Main Author | |
|---|---|
| Format | eBook Book |
| Language | English |
| Published |
Berlin, Heidelberg
Springer Nature
2013
Springer Springer Berlin / Heidelberg Springer Berlin Heidelberg |
| Edition | 1 |
| Series | Lecture Notes in Computer Science |
| Subjects | |
| Online Access | Get full text |
| ISBN | 9783642449581 3642449581 3642449573 9783642449574 |
| ISSN | 0302-9743 1611-3349 |
| DOI | 10.1007/978-3-642-44958-1 |
Cover
| Abstract | This proceedings of the Ray Solomonoff 85th memorial conference, presents 35 papers on universal Bayesian prediction and artificial intelligence (machine learning). A tribute to Solomonoff's work, which influences modern data mining, econometrics and more. |
|---|---|
| AbstractList | This proceedings of the Ray Solomonoff 85th memorial conference, presents 35 papers on universal Bayesian prediction and artificial intelligence (machine learning). A tribute to Solomonoff's work, which influences modern data mining, econometrics and more. Algorithmic probability and friends: Proceedings of the Ray Solomonoff 85th memorial conference is a collection of original work and surveys. The Solomonoff 85th memorial conference was held at Monash University's Clayton campus in Melbourne, Australia as a tribute to pioneer, Ray Solomonoff (1926-2009), honouring his various pioneering works - most particularly, his revolutionary insight in the early 1960s that the universality of Universal Turing Machines (UTMs) could be used for universal Bayesian prediction and artificial intelligence (machine learning). This work continues to increasingly influence and under-pin statistics, econometrics, machine learning, data mining, inductive inference, search algorithms, data compression, theories of (general) intelligence and philosophy of science - and applications of these areas. Ray not only envisioned this as the path to genuine artificial intelligence, but also, still in the 1960s, anticipated stages of progress in machine intelligence which would ultimately lead to machines surpassing human intelligence. Ray warned of the need to anticipate and discuss the potential consequences - and dangers - sooner rather than later. Possibly foremostly, Ray Solomonoff was a fine, happy, frugal and adventurous human being of gentle resolve who managed to fund himself while electing to conduct so much of his paradigm-changing research outside of the university system. The volume contains 35 papers pertaining to the abovementioned topics in tribute to Ray Solomonoff and his legacy. |
| Author | Dowe, David L. |
| Author_xml | – sequence: 1 fullname: Dowe, David L. |
| BackLink | https://cir.nii.ac.jp/crid/1130282269140963200$$DView record in CiNii |
| BookMark | eNpd0U1v1DAQBmADBbGU_QHcIoRUcUg7YzuJfdyu2lKpEhwqrpaTTHZNXXuJw0f_Pc6GC1xsafy8c3j9hp2EGIixdwjnCNBc6EaVoqwlL6XUlSrxGVvnmciT4wCfsxXWiKUQUr_47-2ErUAAL3UjxSu2UrVUHCspXrN1St8AAEFqUakVu9_4XRzdtH90XfFljK1tnXfTU2FDX1yPjkKfzotL-0TJ2ZAF9a6bXAxHsBknN7jOWV_chom8dzsKHb1lLwfrE63_3qfs6_XV_fZTeff55na7uSutUAi_S67aBoEk7y3RMHA-WOhEL2tbyVqh1qiqpq10a4dqgB5gwCFHSRHZnrdKnLKPy2KbHuhX2kc_JfPTUxvjQzL_VJLtxWLTYXRhR6NZFIKZC5-1ESZ7cwyYOXG2JA5j_P6D0mSOizsK02i9ubrcCphb1Fl-WGRwznRuPhHzByjOa40SdC04QGbvF9bZZH1m5jGGuBvtYZ9MJRGbvO8PBX2Qdg |
| ContentType | eBook Book |
| Copyright | Springer-Verlag Berlin Heidelberg 2013 |
| Copyright_xml | – notice: Springer-Verlag Berlin Heidelberg 2013 |
| DBID | I4C RYH |
| DEWEY | 004 |
| DOI | 10.1007/978-3-642-44958-1 |
| DatabaseName | Casalini Torrossa eBooks Institutional Catalogue CiNii Complete |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science Applied Sciences Mathematics |
| DocumentTitleAlternate | Algorithmic probability and friends : Bayesian prediction and artificial intelligence, papers from the Ray Solomonoff 85th memorial conference, Melbourne, VIC, Australia, November/December 2011 |
| EISBN | 9783642449581 3642449581 |
| EISSN | 1611-3349 |
| Edition | 1 2013 |
| Editor | Dowe, David L |
| Editor_xml | – sequence: 1 fullname: Dowe, David L |
| ExternalDocumentID | 9783642449581 321121 EBC3093589 BB14726129 5411709 |
| GroupedDBID | -JY -K2 0D6 0DA 2HV 38. AABBV AARVG AAUBL AAWHR ABBVZ ABFTD ABMNI AEDXK AEHWL AEKFX AEZAY AFJMS ALMA_UNASSIGNED_HOLDINGS ARZOH AZZ BBABE CZZ I4C IEZ IX0 LDH MA. N2R NUC SAO SBO TPJZQ TSXQS Z7R Z7S Z83 Z84 Z88 ACPRQ ADHDZ ADNMO AEJLV AFPTF RYH Z7X -DT -GH -~X 1SB 29L 2HA 5QI 875 AASHB ACGFS ADCXD AEFIE EJD F5P FEDTE HVGLF LAS P2P RNI RSU SVGTG VI1 ~02 |
| ID | FETCH-LOGICAL-a3810x-28b710e42daeeff22fa0c3d46a54681991857b59baf5f0d00f1fa38e8eead2b83 |
| ISBN | 9783642449581 3642449581 3642449573 9783642449574 |
| ISSN | 0302-9743 |
| IngestDate | Fri Nov 08 04:00:08 EST 2024 Wed Sep 17 02:55:11 EDT 2025 Fri May 30 22:52:24 EDT 2025 Fri Jun 27 00:59:03 EDT 2025 Tue Nov 14 22:45:40 EST 2023 |
| IsPeerReviewed | false |
| IsScholarly | false |
| LCCallNum_Ident | Q |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-a3810x-28b710e42daeeff22fa0c3d46a54681991857b59baf5f0d00f1fa38e8eead2b83 |
| Notes | Includes bibliographical references and index |
| OCLC | 864821543 |
| PQID | EBC3093589 |
| PageCount | 457 |
| ParticipantIDs | askewsholts_vlebooks_9783642449581 springer_books_10_1007_978_3_642_44958_1 proquest_ebookcentral_EBC3093589 nii_cinii_1130282269140963200 casalini_monographs_5411709 |
| PublicationCentury | 2000 |
| PublicationDate | 2013 c2013 2013-10-22 |
| PublicationDateYYYYMMDD | 2013-01-01 2013-10-22 |
| PublicationDate_xml | – year: 2013 text: 2013 |
| PublicationDecade | 2010 |
| PublicationPlace | Berlin, Heidelberg |
| PublicationPlace_xml | – name: Netherlands – name: Heidelberg – name: Berlin, Heidelberg |
| PublicationSeriesTitle | Lecture Notes in Computer Science |
| PublicationYear | 2013 |
| Publisher | Springer Nature Springer Springer Berlin / Heidelberg Springer Berlin Heidelberg |
| Publisher_xml | – name: Springer Nature – name: Springer – name: Springer Berlin / Heidelberg – name: Springer Berlin Heidelberg |
| RelatedPersons | Kleinberg, Jon M. Mattern, Friedemann Nierstrasz, Oscar Steffen, Bernhard Kittler, Josef Vardi, Moshe Y. Weikum, Gerhard Sudan, Madhu Naor, Moni Mitchell, John C. Terzopoulos, Demetri Pandu Rangan, C. Kanade, Takeo Hutchison, David Tygar, Doug |
| RelatedPersons_xml | – sequence: 1 givenname: David surname: Hutchison fullname: Hutchison, David organization: Lancaster University, Lancaster, UK – sequence: 2 givenname: Takeo surname: Kanade fullname: Kanade, Takeo organization: Carnegie Mellon University, Pittsburgh, USA – sequence: 3 givenname: Josef surname: Kittler fullname: Kittler, Josef organization: University of Surrey, Guildford, UK – sequence: 4 givenname: Jon M. surname: Kleinberg fullname: Kleinberg, Jon M. organization: Cornell University, Ithaca, USA – sequence: 5 givenname: Friedemann surname: Mattern fullname: Mattern, Friedemann organization: ETH Zurich, Zurich, Switzerland – sequence: 6 givenname: John C. surname: Mitchell fullname: Mitchell, John C. organization: Stanford University, Stanford, USA – sequence: 7 givenname: Moni surname: Naor fullname: Naor, Moni organization: Weizmann Institute of Science, Rehovot, Israel – sequence: 8 givenname: Oscar surname: Nierstrasz fullname: Nierstrasz, Oscar organization: University of Bern, Bern, Switzerland – sequence: 9 givenname: C. surname: Pandu Rangan fullname: Pandu Rangan, C. organization: Indian Institute of Technology, Madras, India – sequence: 10 givenname: Bernhard surname: Steffen fullname: Steffen, Bernhard organization: University of Dortmund, Dortmund, Germany – sequence: 11 givenname: Madhu surname: Sudan fullname: Sudan, Madhu organization: Massachusetts Institute of Technology, USA – sequence: 12 givenname: Demetri surname: Terzopoulos fullname: Terzopoulos, Demetri organization: University of California, Los Angeles, USA – sequence: 13 givenname: Doug surname: Tygar fullname: Tygar, Doug organization: University of California, Berkeley, USA – sequence: 14 givenname: Moshe Y. surname: Vardi fullname: Vardi, Moshe Y. organization: Rice University, Houston, USA – sequence: 15 givenname: Gerhard surname: Weikum fullname: Weikum, Gerhard organization: Max-Planck Institute of Computer Science, Saarbrücken, Germany |
| SSID | ssj0001049358 ssj0002792 |
| Score | 1.9028023 |
| Snippet | This proceedings of the Ray Solomonoff 85th memorial conference, presents 35 papers on universal Bayesian prediction and artificial intelligence (machine... Algorithmic probability and friends: Proceedings of the Ray Solomonoff 85th memorial conference is a collection of original work and surveys. The Solomonoff... |
| SourceID | askewsholts springer proquest nii casalini |
| SourceType | Aggregation Database Publisher |
| SubjectTerms | Algorithms Algorithms -- Congresses Artificial intelligence Artificial intelligence -- Congresses Bayesian statistical decision theory Bayesian statistical decision theory -- Congresses Computer Science Computer Science, general Computer science-Congresses Data processing Computer science Memorial Probabilities Probabilities -- Congresses Ray Solomonoff |
| Subtitle | Papers from the Ray Solomonoff 85th Memorial Conference, Melbourne, VIC, Australia, November 30 - December 2, 2011 |
| TableOfContents | 3 Mirage Codes Using Algorithmic Information Theory -- 4 Probabilistic Automata -- 5 Definitions of Transducers -- 6 Frequency Transducers -- References -- A Critical Survey of Some Competing Accounts of Concrete Digital Computation -- 1 Introduction -- 2 The Formal Symbol Manipulation Account -- 3 The Physical Symbol Systems Account -- 4 The Mechanistic Account of Computation -- 5 Discussion -- 6 Conclusion -- References -- Further Reflections on the Timescale of AI -- References -- Towards Discovering the Intrinsic Cardinality and Dimensionality of Time Series Using MDL -- 1 Introduction -- 2 Definitions and Notation -- 3 MDL Modeling of Time Series -- 4 Experimental Evaluation -- 4.1 An Example Application in Physiology -- 5 Discussion of Time and Space Complexity and Conclusions -- References -- Complexity Measures for Meta-learning and Their Optimality -- 1 Introduction -- 2 Complexity Measures for Learning Machines -- 3 Example of an Application -- 4 Summary -- References -- Design of a Conscious Machine -- Introduction -- 2 Functional Requirements -- 2.1 Worldly Facts -- 2.2 Smaller Factal Groups -- 2.3 Cognitive Functions -- 2.4 Macro Structures of the Whole Cortex (Brodmann) -- 3 Processing in an Information Domain -- 3.1 Caveats -- 4 Implementation Notes -- 4.1 The Modeling Strategy -- 4.2 Coincidence Records -- 4.3 Hippocampal Routing -- References -- No Free Lunch versus Occam's Razor in Supervised Learning -- 1 Introduction -- 2 Preliminaries -- 3 No Free Lunch Theorem -- 4 Complexity-Based Classification -- 5 Discussion -- References -- An Approximation of the Universal Intelligence Measure -- 1 Introduction -- 2 Background -- 2.1 Universal Intelligence Tests -- 2.2 Universal Intelligence Measure -- 3 Algorithmic Intelligence Quotient -- 3.1 Environment Sampling -- 3.2 Environment Simulation -- 3.3 Temporal Preference Intro -- Preface -- Organization -- Table of Contents -- Introduction -- Introduction to Ray Solomonoff 85th Memorial Conference -- 1 Introduction - and Summary -- 1.1 Short Summary -- 1.2 (Universal) Turing Machines and Prediction -- 1.3 Technological Singularity (and Training Sequences) -- 2 Papers - Beginning in 1950 -- 3 Birth of the Theory in 1960 - and Onwards -- 3.1 End of the 1970s, and Fundamental Convergence Result -- 3.2 Notes on Papers from the 1980s -- 3.3 Notes on Papers from the 1990s -- 3.4 Notes on Papers from the 2000s -- 4 Further Notes (And Perhaps Some Afterthoughts) -- 4.1 Uniqueness of Logarithm-Loss Information-Theoretic Cost -- 4.2 Prediction, Inference, Induction, Explanation -- 4.3 How to Choose a Bayesian Prior? -- 4.4 Information Theory, (Artificial) Intelligence and Recognising It -- 4.5 A Music Note -- 4.6 Originality, Creativity, Humour, Illusion -- 4.7 Some Further Work -- 4.8 From Here -- References -- Invited Papers -- Ray Solomonoff and the New Probability -- 1 Introduction -- 2 Early Years -- 3 From the University to the Birth of AI -- 4 The Beginnings of AI -- 5 The Discovery of Algorithmic Probability -- 6 The Guerrilla Workshop -- 7 LaterWork -- References -- Universal Heuristics: How Do Humans Solve "Unsolvable" Problems? -- Partial Match Distance -- 1 Introduction -- 2 Partial Matching -- 3 TheDmin Distance -- 4 Question Answering -- 5 Voice Recognition Correction -- References -- Long Papers -- Falsification and Future Performance -- 1 Introduction -- 2 Measurement -- 2.1 Semantics -- 2.2 Risk -- 3 Statistical Learning Theory -- 4 Falsification -- 4.1 Empirical VC Entropy -- 4.2 Empirical Rademacher Complexity -- 5 Discussion -- References -- The Semimeasure Property of Algorithmic Probability - "Feature" or "Bug"? -- 1 Introduction -- 2 Notation -- 3 Algorithmic Probability (ALP) 3.4 Reference Machine Selection -- 3.5 BF Reference Machine -- 3.6 Variance Reduction Techniques for AIQ Estimation -- 4 Empirical Results -- 4.1 Comparison of Artificial Agents -- 4.2 Measuring Agent Scalability -- 4.3 Environment Distribution -- 5 Related Work and Discussion -- 6 Conclusion -- References -- Minimum Message Length Analysis of the Behrens-Fisher Problem -- 1 Introduction -- 2 Minimum Message Length (MML) -- 3 MML and the Behrens-Fisher Problem -- 3.1 Shared Population Mean -- 3.2 Different Population Means -- 3.3 MML Hypothesis Testing -- 4 Simulation and Discussion -- 5 Extensions -- References -- MMLD Inference of Multilayer Perceptrons -- 1 Introduction -- 2 Minimum Message Length (MML) -- 2.1 The Wallace-Freeman Approximation -- 2.2 The MMLD Approximation -- 3 A General Algorithm for Computing MMLD Codelengths -- 3.1 Spherical Uncertainty Region -- 3.2 Ellipsoidal Uncertainty Region -- 3.3 A Simple Example: Univariate Normal Distribution -- 4 MMLD Inference of Multilayer Perceptrons -- 4.1 Prior Density for the Model Parameters -- 4.2 Prior Density for the Network Architecture -- 5 Discussion and Results -- References -- An Optimal Superfarthingale and Its Convergence over a Computable Topological Space -- 1 Introduction -- 2 Preliminaries -- 2.1 Algorithmic Probability -- 2.2 Algorithmic Randomness -- 2.3 Game-Theoretic Probability -- 2.4 Computable Topology -- 3 OptimalTest -- 3.1 Approximation -- 3.2 Existence of an Optimal Test -- 4 Optimal Integral Test -- 4.1 Computable Bound -- 4.2 Computable Enumeration -- 4.3 The Existence of an Optimal Integral Test -- 5 Optimal Superfarthingale -- 5.1 Effectivization of Game-Theoretic Probability -- 5.2 Convergence to a Measure -- References -- Diverse Consequences of Algorithmic Probability -- 1 Introduction -- 2 Solomonoff Induction -- 3 The Axiomatization of Artificial Intelligence 2.3 ASNF (Abstraction SuperStructuring Normal Form) Theorems 4 The Semimeasure Property of ALP -- 5 ALP's Application to Induction, and the Semimeasure Problem -- 6 "Bug" or "Feature"? -- 7 Another Way of Tackling the Semimeasure Problem -- References -- Inductive Inference and Partition Exchangeability in Classification -- 1 Introduction -- 2 Supervised Predictive Classification under Partition Exchangeability -- 3 Asymptotic Properties of Supervised Classifiers under Partition Exchangeability -- 4 Discussion -- References -- Learning in the Limit: A Mutational and Adaptive Approach -- 1 Introduction -- 2 The First-Order Adaptive Automaton -- 2.1 Notations and Technical Preliminaries -- 2.2 Automata Transformations -- 3 The Second-Order Adaptive Automaton -- 4 Second-Order Adaptive Automata and Learning in the Limit -- 4.1 Illustrating Example -- 5 Conclusion -- 5.1 Future Work -- References -- Algorithmic Simplicity and Relevance -- 1 Complexity, Simplicity and the Human Mind -- 2 Relevance -- 3 Simplicity Theory -- 4 Relevance from an Algorithmic Perspective -- 4.1 First-Order Relevance -- 4.2 Second-Order Relevance -- 5 Examples -- 5.1 The 'Nude Model' Story -- 5.2 The 'Rally' Discussion -- 6 Discussion -- References -- Categorisation as Topographic Mapping between Uncorrelated Spaces -- 1 Introduction -- 2 Topographic Mappings in the Brain -- 2.1 Easy to See Mappings -- 2.2 Continuous and Discrete - Ocular Dominance Stripes -- 3 The Topographic Extrapolation -- 3.1 Measuring Topographicity -- 3.2 Extrapolation -- 3.3 A Normal Similarity Measure -- 3.4 Extrapolation from Highly Topographic Functions -- 3.5 Independently Varying Spaces -- 4 Explaining the Categorical Nature of Language -- 5 Synaesthesia -- 6 Conclusion -- References -- Algorithmic Information Theory and Computational Complexity -- 1 Introduction -- 2 Tools from Algorithmic Information Theory 4 Incremental Machine Learning -- 5 Cognitive Architecture -- 6 Philosophical Foundation and Consequences -- 7 Intellectual Property towards Infinity Point -- 8 Conclusion -- References -- An Adaptive Compression Algorithm in a Deterministic World -- 1 Introduction -- 2 Adaptive Compression -- 3 Excess and the RC-Frontier -- 4 Discussion -- References -- Toward an Algorithmic Metaphysics -- 1 TheToyWorldW -- 2 Things in -- 2.1 Composition and Division -- 2.2 Scattered Objects -- 2.3 Object Overlap and Coincidence -- 3 Properties in -- References -- Limiting Context by Using the Web to Minimize Conceptual Jump Size -- 1 Introduction -- 1.1 Common Sense Knowledge as a Contextual Filter -- 1.2 Subjectivity -- 1.3 What is Conceptual Jump Size -- 2 Our Trials with Commonsense Knowledge -- Self-correcting Universal Dialog System. -- Toward Concept Search and Manipulation. -- Generating Chains of Concepts. -- Evaluating Concept Triplets. -- - -- - -- - -- - -- - -- Limiting Context. -- Experiment and Its Results. -- 3 Object-Oriented Programming between Artificial and Natural Languages -- 4 Conclusions -- References -- Minimum Message Length Order Selection and Parameter Estimation of Moving Average Models -- 1 Introduction -- 1.1 The Minimum Message Length Principle -- 2 Message Lengths of Moving Average Models -- 2.1 Fisher Information Matrix -- 2.2 Minimising the Message Length -- 2.3 Properties of the MML87 Estimator -- 3 Evaluation -- 3.1 Parameter Estimation -- 3.2 Order Selection -- 3.3 The Southern Oscillation Index Time Series -- References -- Abstraction Super-Structuring Normal Forms: Towards a Theory of Structural Induction -- 1 Introduction -- 2 Abstraction Super-Structuring Normal Forms -- 2.1 Generative Grammars and Turing Machines -- 2.2 Structural Induction, Generative Grammars and Motivation |
| Title | Algorithmic Probability and Friends. Bayesian Prediction and Artificial Intelligence |
| URI | http://digital.casalini.it/9783642449581 https://cir.nii.ac.jp/crid/1130282269140963200 https://ebookcentral.proquest.com/lib/[SITE_ID]/detail.action?docID=3093589 http://link.springer.com/10.1007/978-3-642-44958-1 https://www.vlebooks.com/vleweb/product/openreader?id=none&isbn=9783642449581 |
| Volume | 7070 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3db9MwELdYeWEvfIsyhk6IB6QmU77j8EZL0ZgGD2hMe7OcxN4qSlstYXz8g_xb3Dlxkg4kBA-10tS13dyv57vzfTD2HPdwlakwddNAKjdKfO7iNqLc3JNFiS-dFRTv_O59cvgxOjqLz2xJ-Da6pM4Pih9_jCv5H6riPaQrRcn-A2W7QfEGXiN9sUUKY3tN-O3etq7Fy_M1avUX5NhOJWGaZNtNKiVNmYvLyuj6U_ldmSjJzSUdyNTW95jw0qaOWAxyctLz3sgNxfR2YScfJHIX5JD4o9ZaT3hcX0w-k3vuwmQWsfGCxrCqlr2N9PTtzPAda0tpDomuFBUgmYSe-1oVzXVAn5CA0MnU66-qc7efHB8MDRNUJGLLMGENk1sKa5hQYF0WN5V5fmPfQ48N7OlSV1Ry-72q8yCcTv0opfRn2Q7bSVNUu2--mh8dn_b2NVR9wphTOI-dM2wSLvVrsKfcbaLhrTl32a6sPuFGg5tQXZHUIitJwaoohKwWiy2F5NoZuhFNTu6wEYWr3GU31Ooeu22LdEDLs--znwOswAArgDiAFivwEixSoEeK6dEjBYZIcaDBCRBOAHECiBPocQKEE7A4gR4nDnQocQAx4kCHEAcsPmCADwgcIHQ8YKdv5iezQ7et3uFKyhr3zQ14juKrioJSKqV1EGjpFWEZJTKOEk4udzxO8zjLpY61V3qe9jV-VXGF3C3IefiQjVbrlXrEoIziIgjSNAzzPCq4ltorOFc5xU0rX8dj9mxALHG1NJ4GlRhQm_tjtmdpKOhZmIzwlYgjKr6Ujdk-klUUC2p9Os5H0TnJKB9cEiLTGTOwBBdm9NZ5Wsyns8abAId4YYEgmvltTnBchwgFrkSYpQj_8V9m22O3-r_UEzaqL7-ofZR-6_xpC_RfatSlEg |
| linkProvider | Library Specific Holdings |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=book&rft.title=Algorithmic+probability+and+friends+%3A+Bayesian+prediction+and+artificial+intelligence%2C+papers+from+the+Ray+Solomonoff+85th+memorial+conference%2C+Melbourne%2C+VIC%2C+Australia%2C+November+30-December+2%2C+2011&rft.au=Dowe%2C+David+L.&rft.date=2013-01-01&rft.pub=Springer&rft.isbn=9783642449574&rft_id=info:doi/10.1007%2F978-3-642-44958-1&rft.externalDocID=BB14726129 |
| thumbnail_m | http://utb.summon.serialssolutions.com/2.0.0/image/custom?url=https%3A%2F%2Fvle.dmmserver.com%2Fmedia%2F640%2F97836424%2F9783642449581.jpg |
| thumbnail_s | http://utb.summon.serialssolutions.com/2.0.0/image/custom?url=https%3A%2F%2Fmedia.springernature.com%2Fw306%2Fspringer-static%2Fcover-hires%2Fbook%2F978-3-642-44958-1 |