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.
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Notes
- 1.
From the Greek epistēmē meaning ‘knowledge’ and logos for ‘study of’.
- 2.
Socrates (469 BC–399 BC) was one of the classical Greek philosophers who laid the foundation of western philosophy. His work was transcribed by Plato, his student (428–427 BC–348–347 BC).
- 3.
In the Theaetetus, one of Plato’s dialogues about the nature of knowledge.
- 4.
Aristotle (384 BC–322 BC) was a classical Greek philosopher. He was a student of Plato.
- 5.
Emmanuel Kant (1724, 1804) was a German philosopher.
- 6.
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Lalanda, P., McCann, J.A., Diaconescu, A. (2013). The Decision Function. In: Autonomic Computing. Undergraduate Topics in Computer Science. Springer, London. https://doi.org/10.1007/978-1-4471-5007-7_7
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