1 - Introduction
Published online by Cambridge University Press: 05 July 2012
Summary
Knowledge discovery, machine learning, data mining, pattern recognition, and rule invention are all about algorithms that are designed to extract knowledge from data and to describe patterns by rules.
One of the cornerstones of (traditional) artificial intelligence is the assumption that
Intelligent behaviour requires rational, knowledge-based decisive and active processes.
These processes include the acquisition of new knowledge, which we call machine learning or knowledge discovery. However, when talking about knowledge-based systems we first need to explain what we mean by knowledge. If we try to define learning by intelligence, we need to explain intelligence, and if we want to explain intelligence, we need to explain knowledge. Bertrand Russell (1992, 1995) has given a very precise and in our case very helpful (and actually entirely sufficient) definition of knowledge:
Knowledge is the ability to discriminate things from each other.
As a consequence, learning means acquiring the ability to recognise and differentiate between different things. Thus, the process of knowledge acquisition is a process that is initiated and (autonomously) run by a system whose purpose is to learn by itself. L. G. Valiant (1984) said that
Learning means acquiring a program without a programmer.
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- Information
- Relational Knowledge Discovery , pp. 4 - 16Publisher: Cambridge University PressPrint publication year: 2012