Genetic programming may be more powerful than neural networks and other machine learning techniques, able to solve problems in a wider range of disciplines. In this ground-breaking book, John Koza shows how this remarkable paradigm works and provides substantial empirical evidence that solutions to a great variety of problems from many different fields can be found by genetically breeding populations of computer programs. Genetic Programming contains a great many worked examples and includes a sample computer code that will allow readers to run their own programs.In getting computers to solve problems without being explicitly programmed, Koza stresses two points: that seemingly different problems from a variety of fields can be reformulated as problems of program induction, and that the recently developed genetic programming paradigm provides a way to search the space of possible computer programs for a highly fit individual computer program to solve the problems of program induction. Good programs are found by evolving them in a computer against a fitness measure instead of by sitting down and writing them.

    How can we understandmachines that are so small? Nanosystems covers it all: powerand strength, friction and wear, thermal noise and quantumuncertainty. This is the book for starting the next century ofengineering. - Marvin MinskyMIT Science magazine calls Eric Drexler Mr. Nanotechnology.For years, Drexler has stirred controversy by declaring thatmolecular nanotechnology will bring a sweeping technologicalrevolution - delivering tremendous advances in miniaturization, materials, computers, and manufacturing of all kinds. Now, he'swritten a detailed, top-to-bottom analysis of molecular machinery -how to design it, how to analyze it, and how to build it.Nanosystems is the first scientifically detailed description ofdevelopments that will revolutionize most of the industrialprocesses and products currently in use.This groundbreaking work draws on physics and chemistry toestablish basic concepts and analytical tools. The book thendescribes nanomechanical components, devices, and systems, including parallel computers able to execute 1020 instructions persecond and desktop molecular manufacturing systems able to makesuch products. Via chemical and biochemical techniques, proximalprobe instruments, and software for computer-aided moleculardesign, the book charts a path from present laboratory capabilitiesto advanced molecular manufacturing. Bringing together physics, chemistry, mechanical engineering, and computer science, Nanosystems provides an indispensable introduction to theemerging field of molecular nanotechnology.

    The learner is not told which action to take, as in most forms of machine learning, but instead must discover which actions yield the highest reward by trying them. In the most interesting and challenging cases, actions may affect not only the immediate reward, but also the next situation, and through that all subsequent rewards. These two characteristics -- trial-and-error search and delayed reward -- are the most important distinguishing features of reinforcement learning. Reinforcement learning is both a new and a very old topic in AI. The term appears to have been coined by Minsk (1961), and independently in control theory by Walz and Fu (1965). The earliest machine learning research now viewed as directly relevant was Samuel's (1959) checker player, which used temporal-difference learning to manage delayed reward much as it is used today. Of course learning and reinforcement have been studied in psychology for almost a century, and that work has had a very strong impact on the AI/engineering work. One could in fact consider all of reinforcement learning to be simply the reverse engineering of certain psychological learning processes (e.g. operant conditioning and secondary reinforcement). Reinforcement Learning is an edited volume of original research, comprising seven invited contributions by leading researchers.

    Begins with a discussion of some important general aspects of the Bayesian approach such as the choice of prior distribution, particularly noninformative prior distribution, the problem of nuisance parameters and the role of sufficient statistics, followed by many standard problems concerned with the comparison of location and scale parameters. The main thrust is an investigation of questions with appropriate analysis of mathematical results which are illustrated with numerical examples, providing evidence of the value of the Bayesian approach.

    Taking advantage of Tarski's World 4.0, the text skilfully balances the semantic conception of logic with methods of proof. The book contains eleven chapters, in four parts. Part I is about propositional logic, Part II about quantifier logic. Part III contains chapters on set theory and inductive definitions. Part IV contains advanced topics in logic, including topics of importance in applications of logic in computer science. The Language of First-order Logic contains hundreds of problems and exercises for the user to work through.

    The basic questions addressed in this book are: what is the computational nature of cognition, and what role does it play in language and other mental processes?; What are the main characteristics of contemporary computational paradigms for describing cognition and how do they differ from each other?; What are the prospects for building cognition and how do they differ from each other?; and what are the prospects for building an artificial intelligence?

    The book focuses on the philosphical, rather than the technical or technological aspects of artificial intelligence.