Autonomy Oriented Computing / Multiagent Systems, Artificial Societies, and Simulated Organizations Bd.12 (PDF)
Autonomy Oriented Computing explores the important theoretical and practical issues in AOC, by analyzing methodologies and presenting experimental case studies. The book serves as a comprehensive reference source for researchers, scientists, engineers,...
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Autonomy Oriented Computing explores the important theoretical and practical issues in AOC, by analyzing methodologies and presenting experimental case studies. The book serves as a comprehensive reference source for researchers, scientists, engineers, and professionals in all fields concerned with this promising new development in computer science. It can also be used as a main or supplementary text in graduate and undergraduate programs across a broad range of computer-related disciplines, including Robotics and Automation, Amorphous Computing, Image Processing and Computer Vision, Programming Paradigms, Computational Biology, and many others.
The first part of the book, Fundamentals, describes the basic concepts and characteristics of an AOC system, and then it enumerates the critical design and engineering issues faced in AOC system development. The second part of the book, AOC in Depth, provides a detailed analysis of methodologies and case studies to evaluate the use of AOC in problem solving and complex system modeling. The final chapter reviews the essential features of the AOC paradigm and outlines a number of possibilities for future research and development.
Numerous illustrative examples, experimental case studies, and exercises at the end of each chapter of Autonomy Oriented Computing help particularize and consolidate the methodologies and theories as they are presented.
From Autonomy to AOC (p. 3)
1.1. Introduction
Autonomy oriented computing (AOC) is a new bottom-up paradigm for problem solving and complex systems modeling. In this book, our goal is to substantiate this very statement and to demonstrate useful AOC methodologies and applications. But, before we do so, we need to understand some of the most fundamental issues involved: What are the general characteristics of complex systems consisting of autonomous entities?
What types of behavior can a single or a collection of autonomous entities exhibit or generate? How can we give a definition of autonomy based on the notion of behavior? In a bottom-up computing system, how can the property of autonomy be modeled and utilized? What types of problem is such a bottom-up computing paradigm indented to solve?
How different is this AOC paradigm from other previous or current computing paradigms? In this chapter, we will try to answer each of the above questions. These answers will provide a general context for our later discussions on the AOC formalisms, methodologies, and applications.
1.1.1 Complex Multi-Entity Systems
Examples of complex multi-entity systems are plentiful in everyday life. Traffic on motorways is notoriously busy but most drivers seem to have learned the type of skill to avoid almost all kinds of collision, with only few exceptions. Brokers in stock markets seem to have developed a highly sophisticated 'herd- ing' behavior to buy and sell in the wake of market information.
The balance between species of life forms in an ecosystem is equally complex and yet all of them seem to be settled into a dynamical equilibrium, most of the time. These scenarios point to a common phenomenon that can be observed in everyday life - many independent minds can sometimes maintain order in a global sense despite the lack of communication, central planning, or prior arrangement. In contrast,
Without such intensive practice, players will not be able to get the cue from others and defeat is imminent. Even when a team has been playing together for a long time, secret signs have to be given before desired results can be achieved. In the presence of a group of independent minds, team sports must be significantly different from motonvay traffic so that different behavior results. Nature is full of complex systems some of which have been extensively studied from different angles and with different objectives.
Some researchers want to understand the working mechanism of a complex system concerned. Immunologists, for example, want to know the way in which the human im- mune system reacts to antigens [Louzoun et al., 20001. Similarly, economists want to know the factors contributing to the ups and downs in share prices.
The knowledge gained in this way helps scientists predict future systems behavior. Others studying complex systems behavior want to simulate the observed complex behavior and formulate problem solving strategies for hard computational problems, such as global optimization. Computer scientists and matheniaticians have formulated various algorithms based on natural evolution to solve their problems at hand. In general, one wants to be able to explain, predict, reconstruct, and deploy a complex system.
- Autoren: Jiming Liu , XiaoLong Jin , Kwok Ching Tsui
- 2006, 2005, 216 Seiten, Englisch
- Verlag: Springer-Verlag GmbH
- ISBN-10: 1402081227
- ISBN-13: 9781402081224
- Erscheinungsdatum: 02.07.2006
Abhängig von Bildschirmgröße und eingestellter Schriftgröße kann die Seitenzahl auf Ihrem Lesegerät variieren.
- Dateiformat: PDF
- Größe: 11 MB
- Ohne Kopierschutz
- Vorlesefunktion
From the reviews:
“The book contains numerous illustrative examples and experimental case studies, and there is a rich collection of online resources at http://www.comp.hkbu.edu.hk/~jiming, including software (with source code), slides, and links. In this sense, the book has potential as a resource for a graduate course in evolutionary computation…
In summary, as a coherent treatment of a substantial body of work, with implications and challenges for the broader field of emergent behavior, the book contains a fair amount of interesting and thoughtfully presented material, and will be of interest to many. In addition, the care taken by the authors to package the book with commentary on the approach and the place of the work in the fields, and the impressive collection of additional resources, is a model that others should be encouraged to follow.” (L. Sonenberg, Association for Computing Machinery, Reviews.com, Sep 9 2005)
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