The primary goal of this book is unifying and making more widely accessible the vibrant stream of research - spanning more than two decades - on the theory of semi-feasible algorithms. In doing so it demonstrates the richness inherent in central notions of complexity: running time, nonuniform complexity, lowness, and NP-hardness. The book requires neither great mathematical maturity nor an extensive background in computational complexity theory or in computer science. Another aim of this book is to lay out a path along which the reader can quickly reach the frontiers of current research, and meet and engage the many exciting open problems in this area.

Personal motivation. The dream of creating artificial devices that reach or outperform human inteUigence is an old one. It is also one of the dreams of my youth, which have never left me. What makes this challenge so interesting? A solution would have enormous implications on our society, and there are reasons to believe that the AI problem can be solved in my expected lifetime. So, it's worth sticking to it for a lifetime, even if it takes 30 years or so to reap the benefits. The AI problem. The science of artificial intelligence (AI) may be defined as the construction of intelligent systems and their analysis. A natural definition of a system is anything that has an input and an output stream. Intelligence is more complicated. It can have many faces like creativity, solving prob lems, pattern recognition, classification, learning, induction, deduction, build ing analogies, optimization, surviving in an environment, language processing, and knowledge. A formal definition incorporating every aspect of intelligence, however, seems difficult. Most, if not all known facets of intelligence can be formulated as goal driven or, more precisely, as maximizing some utility func tion. It is, therefore, sufficient to study goal-driven AI; e. g. the (biological) goal of animals and humans is to survive and spread. The goal of AI systems should be to be useful to humans.

A practical introduction to the development of proofs and certified programs using Coq. An invaluable tool for researchers, students, and engineers interested in formal methods and the development of zero-fault software.

The book contains a completely new presentation of classical results in the field of Lambda Calculus, together with new results. The text is unique in that it presents a new calculus (Parametric Lambda Calculus) which can be instantiated to obtain already known lambda-calculi. Some properties, which in the literature have been proved separately for different calculi, can be proved once for the Parametric one. The lambda calculi are presented from a Computer Science point of view, with a particular emphasis on their semantics, both operational and denotational.

This textbook guides students through algebraic specification and verification of distributed systems, and some of the most prominent formal verification techniques. The author employs μCRL as the vehicle, a language developed to combine process algebra and abstract data types. The book evolved from introductory courses on protocol verification taught to undergraduate and graduate students of computer science, and the text is supported throughout with examples and exercises. Full solutions are provided in an appendix, while exercise sheets, lab exercises, example specifications and lecturer slides are available on the author's website.

This is the first book on DNA computing, a molecular approach that may revolutionize computing-replacing silicon with carbon and microchips with DNA molecules. The book starts with an introduction to DNA computing, exploring the power of complementarity, the basics of biochemistry, and language and computation theory. It then brings the reader to the most advanced theories develop thus far in this emerging research area.