Computational Intelligence: Collaboration, Fusion and Emergence (Intelligent Systems Reference Library, 1)

From the Back Cover This book is the first in a new series entitled "Intelligent Systems Reference Library". It is a collection of chapters written by leading experts, covering a rich and diverse variety of computer-based techniques, all involving some aspect of computational intelligence (CI). Authors in this collection recognize the limitations of individual paradigms, and propose some practical and novel ways in which different CI techniques can be combined with each other, or with more traditional computational techniques, to produce powerful problem-solving environments. Common themes to be found in the various chapters of this collection include the following: Fusion, Collaboration, and Emergence. Fusion describes the hybridization of two or more techniques, at least one of which will involve CI. Collaboration ensures that the different techniques work effectively together. Finally, Emergence refers to the phenomenon that complex behaviour can arise as a result of collaboration between simple processing elements. The book covers a wide range of cutting edge techniques and applications, and is divided into the following parts: I. Introduction II. Fusing evolutionary algorithms and fuzzy logic III. Adaptive solution schemes IV. Multi-agent systems V. Computer vision VI. Communication for CI systems VII. Artificial immune systems VIII. Parallel evolutionary algorithms IX. CI for clustering and classification It is widely anticipated that autonomous vehicles will have a transformational impact on military forces and will play a key role in many future force structures. As a result, many tasks have already been identified that unmanned systems could undertake more readily than humans. However, for this to occur, such systems will need to be agile, versatile, persistent, reliable, survivable and lethal. This will require many of the vehicles 'cognitive' or higher order functions to be more fully developed, whereas to date only the 'component' or physical functions have been successfully automated and deployed. The book draws upon a broad range of others' work with a view to providing a product that is greater than the sum of its parts. The discussion is intentionally approached from the perspective of improving understanding rather than providing solutions or drawing firm conclusions. Consequently, researchers reading this book with the hope of uncovering some novel theory or approach to automating an unmanned vehicle will be as disappointed as the capability planner who anticipates a catalogue of technical risks and feasibility options against his favoured list of component technologies and potential applications. Nevertheless, it is hoped that both will at least learn something of the other's world and that progress will ensue as a result. For the defence policy and decision maker, this is a "must-read" book which brings together an important technology summary with a considered analysis of future doctrinal, legal and ethical issues in unmanned and autonomous systems. For research engineers and developers of robotics, this book provides a unique perspective on the implications and consequences of our craft; connecting what we do to the deployment and use of the technology in current and future defence systems. Professor Hugh Durrant-Whyte This book is about synergy in computational intelligence (CI). It is a c- lection of chapters that covers a rich and diverse variety of computer-based techniques, all involving some aspect of computational intelligence, but each one taking a somewhat pragmatic view. Many complex problems in the real world require the application of some form of what we loosely call “intel- gence”fortheirsolution. Fewcanbesolvedbythenaiveapplicationofasingle technique, however good it is. Authors in this collection recognize the li- tations of individual paradigms, and propose some practical and novel ways in which di?erent CI techniques can be combined with each other, or with more traditional computational techniques, to produce powerful probl- solving environments which exhibit synergy, i. e., systems in which the whole 1 is greater than the sum of the parts. Computational intelligence is a relatively new term, and there is some d- agreement as to its precise de?nition. Some practitioners limit its scope to schemes involving evolutionary algorithms, neural networks, fuzzy logic, or hybrids of these. For others, the de?nition is a little more?exible, and will include paradigms such as Bayesian belief networks, multi-agent systems, case-based reasoning and so on. Generally, the term has a similar meaning to the well-known phrase “Arti?cial Intelligence” (AI), although CI is p- ceived moreas a “bottom up” approachfrom which intelligent behaviour can emerge,whereasAItendstobestudiedfromthe“topdown”,andderivefrom pondering upon the “meaning of intelligence”. (These and other key issues will be discussed in more detail in Chapter 1. This book is about synergy in computational intelligence (CI). It is a c- lection of chapters that covers a rich and diverse variety of computer-based techniques, all involving some aspect of computational intelligence, but each one taking a somewhat pragmatic view. Many complex problems in the real world require the application of some form of what we loosely call intel- gencefortheirsolution. Fewcanbesolvedbythenaiveapplicationofasingle technique, however good it is. Authors in this collection recognize the li- tations of individual paradigms, and propose some practical and novel ways in which di?erent CI techniques can be combined with each other, or with more traditional computational techniques, to produce powerful probl- solving environments which exhibit synergy, i. e. , systems in which the whole 1 is greater than the sum of the parts . Computational intelligence is a relatively new term, and there is some d- agreement as to its precise de?nition. Some practitioners limit its scope to schemes involving evolutionary algorithms, neural networks, fuzzy logic, or hybrids of these. For others, the de?nition is a little more ?exible, and will include paradigms such as Bayesian belief networks, multi-agent systems, case-based reasoning and so on. Generally, the term has a similar meaning to the well-known phrase Arti?cial Intelligence (AI), although CI is p- ceived moreas a bottom up approachfrom which intelligent behaviour can emerge,whereasAItendstobestudiedfromthetopdown,andderivefrom pondering upon the meaning of intelligence. (These and other key issues will be discussed in more detail in Chapter 1. Research in computational intelligence is directed toward building thinking machines and improving our understanding of intelligence. As evident, the ultimate achievement in this field would be to mimic or exceed human cognitive capabilities including reasoning, recognition, creativity, emotions, understanding, learning and so on. In this book, the authors illustrate an hybrid computational intelligence framework and it applications for various problem solving tasks. Based on tree-structure based encoding and the specific function operators, the models can be flexibly constructed and evolved by using simple computational intelligence techniques. The main idea behind this model is the flexible neural tree, which is very adaptive, accurate and efficient. Based on the pre-defined instruction/operator sets, a flexible neural tree model can be created and evolved. This volume comprises of 6 chapters including an introductory chapter giving the fundamental definitions and the last Chapter provides some important research challenges. Academics, scientists as well as engineers engaged in research, development and application of computational intelligence techniques and data mining will find the comprehensive coverage of this book invaluable. This book draws upon a broad range of research with a view to creating a product greater than the sum of its parts. The topic is intentionally approached from the perspective of improving understanding rather than providing solutions or firm conclusions. Anthony Finn And Steve Scheding. Includes Bibliographical References (p. [214]-227)