Ebook: Kolmogorov Complexity and Computational Complexity

The mathematical theory of computation has given rise to two important ap­ proaches to the informal notion of "complexity": Kolmogorov complexity, usu­ ally a complexity measure for a single object such as a string, a sequence etc., measures the amount of information necessary to describe the object. Compu­ tational complexity, usually a complexity measure for a set of objects, measures the compuational resources necessary to recognize or produce elements of the set. The relation between these two complexity measures has been considered for more than two decades, and may interesting and deep observations have been obtained. In March 1990, the Symposium on Theory and Application of Minimal­ Length Encoding was held at Stanford University as a part of the AAAI 1990 Spring Symposium Series. Some sessions of the symposium were dedicated to Kolmogorov complexity and its relations to the computational complexity the­ ory, and excellent expository talks were given there. Feeling that, due to the importance of the material, some way should be found to share these talks with researchers in the computer science community, I asked the speakers of those sessions to write survey papers based on their talks in the symposium. In response, five speakers from the sessions contributed the papers which appear in this book.

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There are many ways to measure the complexity of a given object, but there are two measures of particular importance in the theory of computing: One is Kolmogorov complexity, which measures the amount of information necessary to describe an object. Another is computational complexity, which measures the computational resources necessary to recognize (or produce) an object. The relation between these two complexity measures has been studied since the 1960s. More recently, the more generalized notion of resource bounded Kolmogorov complexity and its relation to computational complexity have received much attention. Now many interesting and deep observations on this topic have been established. This book consists of four survey papers concerning these recent studies on resource bounded Kolmogorov complexity and computational complexity. It also contains one paper surveying several types of Kolmogorov complexity measures. The papers are based on invited talks given at the AAAI Spring Symposium on Minimal-Length Encoding in 1990. The book is the only collection of survey papers on this subject and provides fundamental information for researchers in the field.

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There are many ways to measure the complexity of a given object, but there are two measures of particular importance in the theory of computing: One is Kolmogorov complexity, which measures the amount of information necessary to describe an object. Another is computational complexity, which measures the computational resources necessary to recognize (or produce) an object. The relation between these two complexity measures has been studied since the 1960s. More recently, the more generalized notion of resource bounded Kolmogorov complexity and its relation to computational complexity have received much attention. Now many interesting and deep observations on this topic have been established. This book consists of four survey papers concerning these recent studies on resource bounded Kolmogorov complexity and computational complexity. It also contains one paper surveying several types of Kolmogorov complexity measures. The papers are based on invited talks given at the AAAI Spring Symposium on Minimal-Length Encoding in 1990. The book is the only collection of survey papers on this subject and provides fundamental information for researchers in the field.
Content:
Front Matter....Pages i-vii
Introduction....Pages 1-3
Applications of Time-Bounded Kolmogorov Complexity in Complexity Theory....Pages 4-22
On Sets with Small Information Content....Pages 23-42
Kolmogorov Complexity, Complexity Cores, and the Distribution of Hardness....Pages 43-65
Resource Bounded Kolmogorov Complexity and Statistical Tests....Pages 66-84
Complexity and Entropy: An Introduction to the Theory of Kolmogorov Complexity....Pages 85-102
Back Matter....Pages 103-106

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There are many ways to measure the complexity of a given object, but there are two measures of particular importance in the theory of computing: One is Kolmogorov complexity, which measures the amount of information necessary to describe an object. Another is computational complexity, which measures the computational resources necessary to recognize (or produce) an object. The relation between these two complexity measures has been studied since the 1960s. More recently, the more generalized notion of resource bounded Kolmogorov complexity and its relation to computational complexity have received much attention. Now many interesting and deep observations on this topic have been established. This book consists of four survey papers concerning these recent studies on resource bounded Kolmogorov complexity and computational complexity. It also contains one paper surveying several types of Kolmogorov complexity measures. The papers are based on invited talks given at the AAAI Spring Symposium on Minimal-Length Encoding in 1990. The book is the only collection of survey papers on this subject and provides fundamental information for researchers in the field.
Content:
Front Matter....Pages i-vii
Introduction....Pages 1-3
Applications of Time-Bounded Kolmogorov Complexity in Complexity Theory....Pages 4-22
On Sets with Small Information Content....Pages 23-42
Kolmogorov Complexity, Complexity Cores, and the Distribution of Hardness....Pages 43-65
Resource Bounded Kolmogorov Complexity and Statistical Tests....Pages 66-84
Complexity and Entropy: An Introduction to the Theory of Kolmogorov Complexity....Pages 85-102
Back Matter....Pages 103-106
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