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"This book explores the foundation, history, and theory of intelligent adaptive systems, providing a fundamental resource on topics such as the emergence of intelligent adaptive systems in social sciences, biologically inspired artificial social systems, sensory information processing, as well as the conceptual and methodological issues and approaches to intelligent adaptive systems"--Provided by publisher.
"This book provides an estimable global view of the most up-to-date research on the strategies, applications, practice, and implications of complex adaptive systems, to better understand the various critical systems that surround human life. Researchers will find this book an indispensable state-of-art reference"--Provided by publisher.
In this book David and Alex Bennet propose a new model for organizations that enables them to react more quickly and fluidly to today's fast-changing, dynamic business environment: the Intelligent Complex Adaptive System (ICAS). ICAS is a new organic model of the firm based on recent research in complexity and neuroscience, and incorporating networking theory and knowledge management, and turns the living system metaphor into a reality for organizations. This book synthesizes new thinking about organizational structure from the fields listed above into ICAS, a new systems model for the successful organization of the future designed to help leaders and managers of knowledge organizations succeed in a non-linear, complex, fast-changing and turbulent environment. Technology enables connectivity, and the ICAS model takes advantage of that connectivity by fostering the development of dynamic, effective and trusting relationships in a new organizational structure. This book outlines the model in chapter four, and then breaks down the model into its components in the next two chapters. This is a benefit to readers since different components of the model can be implemented at different times, so the book can guide implementation of one or all of the components as a manager sees fit. There are eight characteristics of the ICAS: organizational intelligence, unity and shared purpose, optimum complexity, selectivity, knowledge centricity, flow, permeable boundaries, and multi-dimensionality.
This exciting study explores the novel insight, based on well-established ethological principles, that animals, humans, and autonomous robots can all be analyzed as multi-task autonomous control systems.
As ubiquitous as the atmosphere, intelligent adaptive systems (IASs) surround us in our daily lives. When designed well, these systems sense users and their environments so that they can provide support in a manner that is not only responsive to the evolving situation, but unnoticed by the user. A synthesis of recent research and developments on IASs from the human factors (HF) and human–computer interaction (HCI) domains, Intelligent Adaptive Systems: An Interaction-Centered Design Perspective provides integrated design guidance and recommendations for researchers and system developers. The book explores a recognized lack of integration between the HF and HCI research communities, which has led to inconsistencies between the research approaches adopted, and a lack of exploitation of research from one field by the other. The authors integrate theories and methodologies from these domains to provide design recommendations for human–machine developers. They then establish design guidance through the review of conceptual frameworks, analytical methodologies, and design processes for intelligent adaptive systems. The book draws on case studies from the military, medical, and distance learning domains to illustrate intelligent system design to examine lessons learned. Outlining an interaction-centered perspective for designing an IAS, the book details methodologies for understanding human work in complex environments and offers understanding about why and how optimizing human–machine interaction should be central to the design of IASs. The authors present an analytical and design methodology as well as an implementation strategy that helps you choose the proper design framework for your needs.
Author : John Henry Holland Publisher : Oxford University Press, USA Page : 121 pages File Size : 10,75 MB Release : 2014 Category : History ISBN : 0199662541
In this very short introduction, John Holland presents an introduction to the science of complexity. Using examples from biology and economics, he shows how complexity science models the behaviour of complex systems.
Author : John H. Miller Publisher : Princeton University Press Page : 285 pages File Size : 39,52 MB Release : 2009-11-28 Category : Social Science ISBN : 1400835526
This book provides the first clear, comprehensive, and accessible account of complex adaptive social systems, by two of the field's leading authorities. Such systems--whether political parties, stock markets, or ant colonies--present some of the most intriguing theoretical and practical challenges confronting the social sciences. Engagingly written, and balancing technical detail with intuitive explanations, Complex Adaptive Systems focuses on the key tools and ideas that have emerged in the field since the mid-1990s, as well as the techniques needed to investigate such systems. It provides a detailed introduction to concepts such as emergence, self-organized criticality, automata, networks, diversity, adaptation, and feedback. It also demonstrates how complex adaptive systems can be explored using methods ranging from mathematics to computational models of adaptive agents. John Miller and Scott Page show how to combine ideas from economics, political science, biology, physics, and computer science to illuminate topics in organization, adaptation, decentralization, and robustness. They also demonstrate how the usual extremes used in modeling can be fruitfully transcended.
The Bennets offer a new theory of the firm, describing a next-generation organization designed to exhibit flexibility and agility in order to meet the challenges of increasing change, uncertainty, and complexity that characterize the competitive environment.
Genetic algorithms are playing an increasingly important role in studies of complex adaptive systems, ranging from adaptive agents in economic theory to the use of machine learning techniques in the design of complex devices such as aircraft turbines and integrated circuits. Adaptation in Natural and Artificial Systems is the book that initiated this field of study, presenting the theoretical foundations and exploring applications. In its most familiar form, adaptation is a biological process, whereby organisms evolve by rearranging genetic material to survive in environments confronting them. In this now classic work, Holland presents a mathematical model that allows for the nonlinearity of such complex interactions. He demonstrates the model's universality by applying it to economics, physiological psychology, game theory, and artificial intelligence and then outlines the way in which this approach modifies the traditional views of mathematical genetics. Initially applying his concepts to simply defined artificial systems with limited numbers of parameters, Holland goes on to explore their use in the study of a wide range of complex, naturally occuring processes, concentrating on systems having multiple factors that interact in nonlinear ways. Along the way he accounts for major effects of coadaptation and coevolution: the emergence of building blocks, or schemata, that are recombined and passed on to succeeding generations to provide, innovations and improvements.
The "intelligence" of traditional artificial intelligence systems is notoriously narrow and inflexible--incapable of adapting to the constantly changing circumstances of the real world. Although traditional artificial intelligence systems can be successful in narrowly prescribed domains, they are inappropriate for dynamic, complex domains, such as autonomous robot navigation.**This book proposes an alternative methodology for designing intelligent systems based on a model of intelligence as adaptive behavior. The author describes an experiment in computational neuroethology--the computer modeling of neuronal control of behavior--in which the nervous system for an artificial insect is modeled. The experiment demonstrates that simple, complete intelligent agents are able to cope with complex, dynamic environments--suggesting that adaptive models of intelligence, based on biological bases of adaptive behavior, may prove to be very useful in the design of intelligent, autonomous systems. Provides a lucid critique of traditional artificial intelligence research programs Presents new methodology for the construction autonomous agents, which has implications for mobile robotics Of interest to researchers in a variety of fields: artificial intelligence, neural networks, robotics, cognitive science, and neuroscience