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The Decision Function

  • Philippe Lalanda
  • Julie A. McCann
  • Ada Diaconescu
Chapter
Part of the Undergraduate Topics in Computer Science book series (UTICS)

Abstract

In the previous chapters, we saw how self-managed systems could accumulate information about their execution context and how they could adapt their own internal structures. We now focus on the decision function that links sensory inputs to actuating outputs. This function heavily relies on the notion of knowledge (knowledge about the system internals, knowledge about the computing environment, knowledge about ways to solve problems) and as well as the ability to reason about this knowledge. There are many different ways to represent knowledge in computing science, and a wide range of reasoning techniques have been proposed, in particular in the artificial intelligence community.

The purpose of this section is to present different knowledge representations and associated reasoning techniques well suited to autonomic systems. It is not meant to be exhaustive. In fact, there is no such thing as a general knowledge representation of reasoning approach for autonomic management. Depending on the requirements, different formalisms and techniques with different properties can be selected.

Keywords

Bayesian Network Autonomic System Knowledge Representation Propositional Logic Architectural Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  • Philippe Lalanda
    • 1
  • Julie A. McCann
    • 2
  • Ada Diaconescu
    • 3
  1. 1.Laboratoire Informatique de GrenobleUniversité Joseph FourierGrenobleFrance
  2. 2.Department of ComputingImperial College LondonLondonUK
  3. 3.Department of Computing and NetworkingTélécom ParisTechParisFrance

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