By Jiming Liu
Autonomy orientated Computing is a complete reference for scientists, engineers, and different execs all in favour of this promising improvement in laptop technological know-how. it could even be used as a textual content in graduate/undergraduate courses in a huge variety of computer-related disciplines, together with Robotics and Automation, Amorphous Computing, photograph Processing, Programming Paradigms, Computational Biology, and so on.
Part One describes the fundamental thoughts and features of an AOC procedure and enumerates the severe layout and engineering concerns confronted in AOC approach improvement. half offers specified analyses of methodologies and case experiences to judge AOC utilized in challenge fixing and intricate method modeling. the ultimate bankruptcy outlines chances for destiny examine and development.
Numerous illustrative examples, experimental case stories, and workouts on the finish of every bankruptcy of Autonomy orientated Computing support particularize and consolidate the methodologies and theories presented.
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Additional resources for Autonomy Oriented Computing: From Problem Solving to Complex Systems Modeling
3. 17 Autonomy Oriented Search In our next example, let us consider the following search problem: An environment contains a homogeneous region with the same physical feature. This region is referred to as a goal region. The feature of the goal can be evaluated based on some measurements. Here, the term ‘measurement’ is taken as a generic notion. The specific quantity that it refers to depends on the nature of applications. For instance, it may refer to the grey level intensity of an image in the case of image processing.
The logic paradigm is to implement a general relation. It describes a set of features that a solution should have rather than a set of steps to obtain such a solution. The object oriented programming (OOP) paradigm is built on the imperative paradigm. It can be viewed as an extension of the imperative paradigm. It encapsulates variables and their operations as classes. As inspired by the object oriented computing paradigm, Shoham proposed an agent oriented programming (AOP) paradigm [Shoham, 1993], where the basic element is an agent characterized by a group of mental parameters, such as beliefs, commitments, and choices.
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.
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