Autonomy Oriented Computing / Multiagent Systems, Artificial Societies, and Simulated Organizations Bd.12 (PDF)

Chapter 1
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,

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)