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Algorithmic Randomness and Complexity
Computability and complexity theory are two central areas of research in theoretical computer science. This book provides a systematic, technical development of "algorithmic randomness" and complexity for scientists from diverse fields.
Computability and Complexity
This is a book about computation, something which is ubiquitous in the modern world. More precisely, it examines computability theory and computational complexity theory. Computability theory is the part of mathematics and computer science which seeks to clarify what we mean by computation or algorithm. When is there a computational solution possible to some question? How can we show that none is possible? How computationally hard is the question we are concerned with? Arguably, this area lead to the development of digital computers. (Computational) complexity theory is an intellectual heir of computability theory. Complexity theory is concerned with understanding what resources are needed for computation, where typically we would measure the resources in terms of time and space. Can we perform some task in a feasible number of steps? Can we perform some algorithm with only a limited memory? Does randomness help? Are there standard approaches to overcoming computational difficulty?
Proceedings of the 12th Asian Logic Conference, Wellington, New Zealand, 15-20 December 2011
The Asian Logic Conference is one of the largest meetings, and this volume represents work presented at, and arising from the 12th meeting. It collects a number of interesting papers from experts in the field. It covers many areas of logic.
Parameterized Complexity
An approach to complexity theory which offers a means of analysing algorithms in terms of their tractability. The authors consider the problem in terms of parameterized languages and taking "k-slices" of the language, thus introducing readers to new classes of algorithms which may be analysed more precisely than was the case until now. The book is as self-contained as possible and includes a great deal of background material. As a result, computer scientists, mathematicians, and graduate students interested in the design and analysis of algorithms will find much of interest.
Computational Logic
Handbook of the History of Logic brings to the development of logic the best in modern techniques of historical and interpretative scholarship. Computational logic was born in the twentieth century and evolved in close symbiosis with the advent of the first electronic computers and the growing importance of computer science, informatics and artificial intelligence. With more than ten thousand people working in research and development of logic and logic-related methods, with several dozen international conferences and several times as many workshops addressing the growing richness and diversity of the field, and with the foundational role and importance these methods now assume in mathematic...
Algorithmic Randomness
Surveys on recent developments in the theory of algorithmic randomness and its interactions with other areas of mathematics.
Nanonetworks
Learn the basics—and more—of nanoscale computation and communication in this emerging and interdisciplinary field The field of nanoscale computation and communications systems is a thriving and interdisciplinary research area which has made enormous strides in recent years. A working knowledge of nanonetworks, their conceptual foundations, and their applications is an essential tool for the next generation of scientists and network engineers. Nanonetworks: The Future of Communication and Computation offers a thorough, accessible overview of this subject rooted in extensive research and teaching experience. Offering a concise and intelligible introduction to the key paradigms of nanoscale...
Theory and Applications of Models of Computation
TAMC 2006 was the third conference in the series. The previous two meetings were held May 17–19, 2004 in Beijing, and May 17–20, 2005 in Kunming
Computational Complexity of some Optimization Problems in Planning
Automated planning is known to be computationally hard in the general case. Propositional planning is PSPACE-complete and first-order planning is undecidable. One method for analyzing the computational complexity of planning is to study restricted subsets of planning instances, with the aim of differentiating instances with varying complexity. We use this methodology for studying the computational complexity of planning. Finding new tractable (i.e. polynomial-time solvable) problems has been a particularly important goal for researchers in the area. The reason behind this is not only to differentiate between easy and hard planning instances, but also to use polynomial-time solvable instances...
Reliability and Maintenance
Reliability and Maintenance: Networks and Systems gives an up-to-date presentation of system and network reliability analysis as well as maintenance planning with a focus on applicable models. Balancing theory and practice, it presents state-of-the-art research in key areas of reliability and maintenance theory and includes numerous examples and ex
