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This book brings new scientific methods to intelligence research that is still under the influence of 19th century single causal theory and method. The author describes a rigorous and exhaustive classification of natural intelligence while demonstrating a more adequate scientific and mathematical approach than current statistical and psychometric approaches construct to shore up the out-dated and misused IQ hypothetical. The author demonstrates the superiority of a highly developed multidisciplinary-theory models view of intelligence.
The synchronized flashing of fireflies at night. The spiraling patterns of an aggregating slime mold. The anastomosing network of army-ant trails. The coordinated movements of a school of fish. Researchers are finding in such patterns--phenomena that have fascinated naturalists for centuries--a fertile new approach to understanding biological systems: the study of self-organization. This book, a primer on self-organization in biological systems for students and other enthusiasts, introduces readers to the basic concepts and tools for studying self-organization and then examines numerous examples of self-organization in the natural world. Self-organization refers to diverse pattern formation processes in the physical and biological world, from sand grains assembling into rippled dunes to cells combining to create highly structured tissues to individual insects working to create sophisticated societies. What these diverse systems hold in common is the proximate means by which they acquire order and structure. In self-organizing systems, pattern at the global level emerges solely from interactions among lower-level components. Remarkably, even very complex structures result from the iteration of surprisingly simple behaviors performed by individuals relying on only local information. This striking conclusion suggests important lines of inquiry: To what degree is environmental rather than individual complexity responsible for group complexity? To what extent have widely differing organisms adopted similar, convergent strategies of pattern formation? How, specifically, has natural selection determined the rules governing interactions within biological systems? Broad in scope, thorough yet accessible, this book is a self-contained introduction to self-organization and complexity in biology--a field of study at the forefront of life sciences research.
Despite endless change and disruption, massive upheaval and cosmic collisions, nature has survived the worst of times and thrived in the best of them for 3.8 billion years. She knows what works, what lasts and what contributes to the future of life on Earth. She is the undisputed master of continuous innovation, adaptation and, ultimately, regeneration. What if we humans could tap into the power of the Natural Intelligence that stood the test of time and model our businesses after the proven success stories of nature? What if we could fast track innovation and develop responsible products and agile organisations? We might learn to become life-friendly and self-renewing right where we are and transform our current degenerative value system into a regenerative one. This may sound like science fiction, but is already happening. In this book, Leen Gorissen, PhD in Biology, covers breakthrough insights from the life sciences and how these change the way we look at change and innovation. She shares some of the most advanced thinking and novelties in bio-inspired innovation - covering disciplines like biomimicry, biophilia, permaculture, living systems thinking, nature-based solutions and regenerative design - and clusters these nature-inspired disciplines under the umbrella of NI. Because nature is the largest R&D project in history. Millions of years of field tests have led to designs that outclass any man-made design in terms of efficiency, effectiveness, adaptability, resiliency and endurance. By tapping into the potential of NI, the business world can become an important engine of planetary regeneration and a beacon of creativity and meaningful work spreading hope and ingenuity, not despair and burn-out.
This book brings together the current state of-the-art research in Self Organizing Migrating Algorithm (SOMA) as a novel population-based evolutionary algorithm, modeled on the predator-prey relationship, by its leading practitioners. As the first ever book on SOMA, this book is geared towards graduate students, academics and researchers, who are looking for a good optimization algorithm for their applications. This book presents the methodology of SOMA, covering both the real and discrete domains, and its various implementations in different research areas. The easy-to-follow and implement methodology used in the book will make it easier for a reader to implement, modify and utilize SOMA.
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"This book explores a specific set of intelligence theories, unifying and quantifying to create a verifiable model of various inferencing habits"--Provided by publisher.
Social insects--ants, bees, termites, and wasps--can be viewed as powerful problem-solving systems with sophisticated collective intelligence. Composed of simple interacting agents, this intelligence lies in the networks of interactions among individuals and between individuals and the environment. A fascinating subject, social insects are also a powerful metaphor for artificial intelligence, and the problems they solve--finding food, dividing labor among nestmates, building nests, responding to external challenges--have important counterparts in engineering and computer science. This book provides a detailed look at models of social insect behavior and how to apply these models in the design of complex systems. The book shows how these models replace an emphasis on control, preprogramming, and centralization with designs featuring autonomy, emergence, and distributed functioning. These designs are proving immensely flexible and robust, able to adapt quickly to changing environments and to continue functioning even when individual elements fail. In particular, these designs are an exciting approach to the tremendous growth of complexity in software and information. Swarm Intelligence draws on up-to-date research from biology, neuroscience, artificial intelligence, robotics, operations research, and computer graphics, and each chapter is organized around a particular biological example, which is then used to develop an algorithm, a multiagent system, or a group of robots. The book will be an invaluable resource for a broad range of disciplines.
This book is multi- and interdisciplinary in both scope and content. It draws upon philosophy, the neurosciences, psychology, computer science, and engineering in efforts to resolve fundamental issues about the nature of immediate awareness. Approximately the first half of the book is addressed to historical approaches to the question whether or not there is such a thing as immediate awareness, and if so, what it might be. This involves reviewing arguments that one way or another have been offered as answers to the question or ways of avoiding it. It also includes detailed discussions of some complex questions about the part immediate awareness plays in our over-all natural intelligence. The second half of the book addresses intricate and complex issues involved in the computability of immediate awareness as it is found in simple, ordinary things human beings know how to do, as weIl as in some highly extraordinary things some know how to do. Over the past 2,500 years, human culture has discovered, created, and built very powerful tools for recognizing, classifying, and utilizing patterns found in the natural world. The most powerful of those tools is mathematics, the language of nature. The natural phenomenon of human knowing, of natural intelligence generally, is a very richly textured set of patterns that are highly complex, dynamic, self-organizing, and adaptive.
The LNCS series reports state-of-the-art results in computer science research, development, and education, at a high level and in both printed and electronic form. Enjoying tight cooperation with the R&D community, with numerous individuals, as well as with prestigious organizations and societies, LNCS has grown into the most comprehensive computer science research forum available. The scope of LNCS, including its subseries LNAI and LNBI, spans the whole range of computer science and information technology including interdisciplinary topics in a variety of application fields.
A groundbreaking interpretation of evolution as the work of Nature’s intelligence • Refutes the orthodox view of evolution as a mindless process driven by chance • Explains why context is more important than mutation in evolutionary innovation • Shows how, by recognizing Nature’s innovative and creative powers, we can overcome our social and environmental challenges with a new green science of evolution Darwin’s theory of evolution is undoubtedly one of the most important scientific ideas of the modern age, explaining the existence of both life and consciousness without recourse to divine intervention. Yet how do we interpret evolution? How do we evaluate the ability of Nature to engineer something as exquisite as the genetic code or the human brain? Could it be that evolution is an intelligent process? Is Nature smart? According to most scientists, the answer is no. While humanity may be intelligent and purposeful, the natural processes that crafted us are deemed to be devoid of such attributes. In a radical move away from orthodoxy, Simon G. Powell extends Darwin’s vision by showing that evolution is not just about the survival of the fittest but rather the survival of clever and sensible behavior. Revealing the importance of the context in which things evolve, he explores the intelligent learning process behind natural selection. Rich with examples of the incredibly complex plants, animals, insects, and marine life designed by Nature--from the carnivorous Venus flytrap and the fungus-farming leafcutter ant to the symbiotic microbes found inside the common cow--he shows Nature as a whole to be a system of self-organizing intelligence in which life and consciousness were always destined to emerge. Examining the origins of life and the failure of artificial intelligence to compete with natural intelligence, he explains how our scientifically narrow-minded views on intelligence are now acting as a barrier to our own evolution. As Darwin’s unfinished business comes to light and Nature’s intelligence is embraced, we learn that Nature’s agenda is not simply the replication of genetic matter but of expanding consciousness. By working with Nature’s creative and innovative powers instead of against them, we can address today’s social and environmental challenges with a new green science of evolution.