Zenil, Hector
Overview
Works:  9 works in 61 publications in 2 languages and 3,123 library holdings 

Genres:  Conference papers and proceedings 
Roles:  Editor, Author, Other 
Classifications:  QA274, 003.54 
Publication Timeline
.
Most widely held works by
Hector Zenil
Randomness through computation : some answers, more questions(
Book
)
16 editions published in 2011 in English and held by 149 WorldCat member libraries worldwide
The scope of Randomness Through Computation is novel Contributors share their personal views and anecdotes on the various reasons and motivations which led them to the study of Randomness. Using a question and answer format, they share their visions from their several distinctive vantage points
16 editions published in 2011 in English and held by 149 WorldCat member libraries worldwide
The scope of Randomness Through Computation is novel Contributors share their personal views and anecdotes on the various reasons and motivations which led them to the study of Randomness. Using a question and answer format, they share their visions from their several distinctive vantage points
A computable universe : understanding and exploring nature as computation by
Hector Zenil(
Book
)
19 editions published between 2012 and 2013 in English and held by 103 WorldCat member libraries worldwide
This volume, with a foreword by Sir Roger Penrose, discusses the foundations of computation in relation to nature. It focuses on two main questions: What is computation? How does nature compute?The contributors are worldrenowned experts who have helped shape a cuttingedge computational understanding of the universe. They discuss computation in the world from a variety of perspectives, ranging from foundational concepts to pragmatic models to ontological conceptions and philosophical implications. The volume provides a stateoftheart collection of technical papers and nontechnical essays, re
19 editions published between 2012 and 2013 in English and held by 103 WorldCat member libraries worldwide
This volume, with a foreword by Sir Roger Penrose, discusses the foundations of computation in relation to nature. It focuses on two main questions: What is computation? How does nature compute?The contributors are worldrenowned experts who have helped shape a cuttingedge computational understanding of the universe. They discuss computation in the world from a variety of perspectives, ranging from foundational concepts to pragmatic models to ontological conceptions and philosophical implications. The volume provides a stateoftheart collection of technical papers and nontechnical essays, re
Irreducibility and computational equivalence : 10 years after Wolfram's A new kind of science by
Hector Zenil(
Book
)
10 editions published between 2013 and 2015 in English and held by 39 WorldCat member libraries worldwide
It is clear that computation is playing an increasingly prominent role in the development of mathematics, as well as in the natural and social sciences. The work of Stephen Wolfram over the last several decades has been a salient part in this phenomenon helping founding the field of Complex Systems, with many of his constructs and ideas incorporated in his book A New Kind of Science (ANKS) becoming part of the scientific discourse and general academic knowledgefrom the now established Elementary Cellular Automata to the unconventional concept of mining the Computational Universe from today's widespread Wolfram's Behavioural Classification to his principles of Irreducibility and Computational Equivalence
10 editions published between 2013 and 2015 in English and held by 39 WorldCat member libraries worldwide
It is clear that computation is playing an increasingly prominent role in the development of mathematics, as well as in the natural and social sciences. The work of Stephen Wolfram over the last several decades has been a salient part in this phenomenon helping founding the field of Complex Systems, with many of his constructs and ideas incorporated in his book A New Kind of Science (ANKS) becoming part of the scientific discourse and general academic knowledgefrom the now established Elementary Cellular Automata to the unconventional concept of mining the Computational Universe from today's widespread Wolfram's Behavioural Classification to his principles of Irreducibility and Computational Equivalence
How nature works : complexity in interdisciplinary research and applications by
Ivan Zelinka(
Book
)
9 editions published in 2014 in English and held by 22 WorldCat member libraries worldwide
This book is based on the outcome of the ""2012 Interdisciplinary Symposium on Complex Systems"" held at the island of Kos. The book consists of 12 selected papers of the symposium starting with a comprehensive overview and classification of complexity problems, continuing by chapters about complexity, its observation, modeling and its applications to solving various problems including reallife applications. More exactly, readers will have an encounter with the structural complexity of vortex flows, the use of chaotic dynamics within evolutionary algorithms, complexity in synthetic biology. 
9 editions published in 2014 in English and held by 22 WorldCat member libraries worldwide
This book is based on the outcome of the ""2012 Interdisciplinary Symposium on Complex Systems"" held at the island of Kos. The book consists of 12 selected papers of the symposium starting with a comprehensive overview and classification of complexity problems, continuing by chapters about complexity, its observation, modeling and its applications to solving various problems including reallife applications. More exactly, readers will have an encounter with the structural complexity of vortex flows, the use of chaotic dynamics within evolutionary algorithms, complexity in synthetic biology. 
Randomness through computation some answers, more questions(
)
1 edition published in 2011 in English and held by 7 WorldCat member libraries worldwide
Pt. I. Stochastic randomness and probabilistic deliberations. 1. Is randomness necessary? / R. Graham. 2. Probability is a lot of logic at once : If you don't know which one to pick, Take 'em all / T. Toffoli. 3. Statistical testing of randomness : New and old procedures / A. L. Rukhin. 4. Scatter and regularity imply Benford's Law ... and more / N. Gauvrit & J.P. Delahaye  pt. II. Randomness and computation in connection to the physical world. 5. Some bridging results and challenges in classical, quantum and computational randomness / G. Longo, C. Palamidessi & T. Paul. 6. Metaphysics, metamathematics and metabiology / G. Chaitin. 7. Uncertainty in physics and computation / M. A. Stay. 8. Indeterminism and randomness through physics / K. Svozil. 9. The MartinLofChaitin thesis : The identification by recursion theory of the mathematical notion of random sequence / J.P. Delahaye. 10. The road to intrinsic randomness / S. Wolfram  pt. III. Algorithmic inference and artificial intelligence. 11. Algorithmic probability  Its discovery  Its properties and application to strong AI / R. J. Solomonoff. 12. Algorithmic randomness as foundation of inductive reasoning and artificial intelligence / M. Hutter. 13. Randomness, Occam's Razor, AI, creativity and digital physics / J. Schmidhuber  pt. IV. Randomness, information and computability. 14. Randomness everywhere : My path to algorithmic information theory / C. S. Calude. 15. The impact of algorithmic information theory on our current views on complexity, randomness, information and prediction / P. Gacs. 16. Randomness, computability and information / J. S. Miller. 17. Studying randomness through computation / A. Nies. 18. Computability, algorithmic randomness and complexity / R. G. Downey. 19. Is randomness native to computer science? Ten years after / M. FerbusZanda & S. Grigorieff  pt. V. Computational complexity, randomized algorithms and applications. 20. Randomness as circuit complexity (and the connection to pseudorandomness) / E. Allender. 21. Randomness : A tool for constructing and analyzing computer programs / A. Kucera. 22. Connecting randomness to computation / M. Li. 23. From errorcorrecting codes to algorithmic information theory / L. Staiger. 24. Randomness in algorithms / O. Watanabe  pt. VI. Panel discussions (Transcriptions). 25. Is the Universe random? / C. S. Calude ... [et al.]. 26. What is computation? (How) does nature compute? / C. S. Calude ... [et al.]
1 edition published in 2011 in English and held by 7 WorldCat member libraries worldwide
Pt. I. Stochastic randomness and probabilistic deliberations. 1. Is randomness necessary? / R. Graham. 2. Probability is a lot of logic at once : If you don't know which one to pick, Take 'em all / T. Toffoli. 3. Statistical testing of randomness : New and old procedures / A. L. Rukhin. 4. Scatter and regularity imply Benford's Law ... and more / N. Gauvrit & J.P. Delahaye  pt. II. Randomness and computation in connection to the physical world. 5. Some bridging results and challenges in classical, quantum and computational randomness / G. Longo, C. Palamidessi & T. Paul. 6. Metaphysics, metamathematics and metabiology / G. Chaitin. 7. Uncertainty in physics and computation / M. A. Stay. 8. Indeterminism and randomness through physics / K. Svozil. 9. The MartinLofChaitin thesis : The identification by recursion theory of the mathematical notion of random sequence / J.P. Delahaye. 10. The road to intrinsic randomness / S. Wolfram  pt. III. Algorithmic inference and artificial intelligence. 11. Algorithmic probability  Its discovery  Its properties and application to strong AI / R. J. Solomonoff. 12. Algorithmic randomness as foundation of inductive reasoning and artificial intelligence / M. Hutter. 13. Randomness, Occam's Razor, AI, creativity and digital physics / J. Schmidhuber  pt. IV. Randomness, information and computability. 14. Randomness everywhere : My path to algorithmic information theory / C. S. Calude. 15. The impact of algorithmic information theory on our current views on complexity, randomness, information and prediction / P. Gacs. 16. Randomness, computability and information / J. S. Miller. 17. Studying randomness through computation / A. Nies. 18. Computability, algorithmic randomness and complexity / R. G. Downey. 19. Is randomness native to computer science? Ten years after / M. FerbusZanda & S. Grigorieff  pt. V. Computational complexity, randomized algorithms and applications. 20. Randomness as circuit complexity (and the connection to pseudorandomness) / E. Allender. 21. Randomness : A tool for constructing and analyzing computer programs / A. Kucera. 22. Connecting randomness to computation / M. Li. 23. From errorcorrecting codes to algorithmic information theory / L. Staiger. 24. Randomness in algorithms / O. Watanabe  pt. VI. Panel discussions (Transcriptions). 25. Is the Universe random? / C. S. Calude ... [et al.]. 26. What is computation? (How) does nature compute? / C. S. Calude ... [et al.]
William Shakespeare by
Hector Zenil(
Book
)
2 editions published in 2005 in Spanish and held by 5 WorldCat member libraries worldwide
2 editions published in 2005 in Spanish and held by 5 WorldCat member libraries worldwide
Une approche expérimentale à la théorie algorithmique de la complexité by
Hector Zenil(
)
1 edition published in 2011 in English and held by 1 WorldCat member library worldwide
Une caractéristique contraignante de la complexité de KolmogorovChaitin (dénotée dans ce chapitre par K) est qu'elle n'est pas calculable à cause du problème de l'arrêt, ce qui limite son domaine d'application. Une autre critique concerne la dépendance de K à un langage particulier ou une machine de Turing universelle particulière, surtout pour les suites assez courtes, par exemple, plus courtes que les longueurs typiques des compilateurs des langages de programmation. En pratique, on peut obtenir une approximation de K(s), grâce à des méthodes de compression. Mais les performances de ces méthodes de compression s'écroulent quand il s'agit des suites courtes. Pour les courtes suites, approcher K(s) par des méthodes de compression ne fonctionne pas. On présente dans cet thèse une approche empirique pour surmonter ce problème. Nous proposons une méthode "naturelle" qui permet d'envisager une définition plus stable de la complexité de KolmogorovChaitin K(s) via la mesure de probabilité algorithmique m(s). L'idée est de faire fonctionner une machine universelle en lui donnant un programme au hasard pour calculer expérimentalement la probabilité m(s) (la probabilité de produire s), pour ensuite évaluer numériquement K(s) de manière alternative aux méthodes des algorithmes de compression. La méthode consiste à : (a) faire fonctionner des machines de calcul (machines de Turing, automates cellulaires) de façon systématique pour produire des suites (b) observer quelles sont les distributions de probabilité obtenues et puis (c) obtenir K(s) à partir de m(s) par moyen du théorème de codage de LevinChaitin
1 edition published in 2011 in English and held by 1 WorldCat member library worldwide
Une caractéristique contraignante de la complexité de KolmogorovChaitin (dénotée dans ce chapitre par K) est qu'elle n'est pas calculable à cause du problème de l'arrêt, ce qui limite son domaine d'application. Une autre critique concerne la dépendance de K à un langage particulier ou une machine de Turing universelle particulière, surtout pour les suites assez courtes, par exemple, plus courtes que les longueurs typiques des compilateurs des langages de programmation. En pratique, on peut obtenir une approximation de K(s), grâce à des méthodes de compression. Mais les performances de ces méthodes de compression s'écroulent quand il s'agit des suites courtes. Pour les courtes suites, approcher K(s) par des méthodes de compression ne fonctionne pas. On présente dans cet thèse une approche empirique pour surmonter ce problème. Nous proposons une méthode "naturelle" qui permet d'envisager une définition plus stable de la complexité de KolmogorovChaitin K(s) via la mesure de probabilité algorithmique m(s). L'idée est de faire fonctionner une machine universelle en lui donnant un programme au hasard pour calculer expérimentalement la probabilité m(s) (la probabilité de produire s), pour ensuite évaluer numériquement K(s) de manière alternative aux méthodes des algorithmes de compression. La méthode consiste à : (a) faire fonctionner des machines de calcul (machines de Turing, automates cellulaires) de façon systématique pour produire des suites (b) observer quelles sont les distributions de probabilité obtenues et puis (c) obtenir K(s) à partir de m(s) par moyen du théorème de codage de LevinChaitin
Une approche expérimentale à la théorie algorithmique de la complexité by
Hector Zenil(
Book
)
1 edition published in 2011 in English and held by 1 WorldCat member library worldwide
In practice, it is a known problem that one cannot compress short strings, shorter, for example, than the length in bits of the compression program which is added to the compressed version of s, making the result (the program producing s) sensitive to the compressor choice and the parameters involved. However, short strings are quite often the kind of data encountered in many practical settings. While compressors' asymptotic behavior guarantees the eventual convergence to K(s) thanks to the invariance theorem, measurements differ considerably in the domain of short strings. We describe a method that combines several theoretical and experimental results to numerically approximate the algorithmic (KolmogorovChaitin) complexity of short bit strings. This is done by an exhaustive execution of abstract machines for which the halting times are known thanks to the Busy Beaver problem. An output frequency distribution is then computed, from which the algorithmic probability is calculated and the algorithmic complexity evaluated by way of the (LevinChaitin) coding theorem. The approach we adopt here is different and independent of the machine size (small machines are used only because they allow us to calculate all of them up to a small size) and relies only on the concept of algorithmic probability. The result is a novel approach that we put forward for numerically calculate the complexity of short strings as an alternative to the indirect method using compression algorithms
1 edition published in 2011 in English and held by 1 WorldCat member library worldwide
In practice, it is a known problem that one cannot compress short strings, shorter, for example, than the length in bits of the compression program which is added to the compressed version of s, making the result (the program producing s) sensitive to the compressor choice and the parameters involved. However, short strings are quite often the kind of data encountered in many practical settings. While compressors' asymptotic behavior guarantees the eventual convergence to K(s) thanks to the invariance theorem, measurements differ considerably in the domain of short strings. We describe a method that combines several theoretical and experimental results to numerically approximate the algorithmic (KolmogorovChaitin) complexity of short bit strings. This is done by an exhaustive execution of abstract machines for which the halting times are known thanks to the Busy Beaver problem. An output frequency distribution is then computed, from which the algorithmic probability is calculated and the algorithmic complexity evaluated by way of the (LevinChaitin) coding theorem. The approach we adopt here is different and independent of the machine size (small machines are used only because they allow us to calculate all of them up to a small size) and relies only on the concept of algorithmic probability. The result is a novel approach that we put forward for numerically calculate the complexity of short strings as an alternative to the indirect method using compression algorithms
Audience Level
0 

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Related Identities
 Penrose, Roger Author of introduction
 Zelinka, Ivan Author Editor
 Rössler, Otto E. Editor
 Sanayei, Ali Editor
 SpringerLink (Online service)
 World Scientific (Firm)
 École doctorale Sciences pour l'Ingénieur (Lille)
 Delahaye, JeanPaul (1952....). Thesis advisor
 Université Lille 1  Sciences et technologies Degree grantor
 Cortés, Enrique