AI - a Guide to Intelligent Systems.pdf - Member of EEPIS

Knowledge engineering and
data mining
9
In which we discuss how to pick the right tool for the job, build an
intelligent system and turn data into knowledge.
9.1 Introduction, or what is knowledge engineering?
Choosing the right tool for the job is undoubtedly the most critical part of
building an intelligent system. Having read this far, you are now familiar with
rule- and frame-based expert systems, fuzzy systems, artificial neural networks,
genetic algorithms, and hybrid neuro-fuzzy and fuzzy evolutionary systems.
Although several of these tools handle many problems well, selecting the one
best suited to a particular problem can be difficult. Davis’s law states: ‘For every
tool there is a task perfectly suited to it’ (Davis and King, 1977). However, it
would be too optimistic to assume that for every task there is a tool perfectly
suited to it. In this chapter, we suggest basic guidelines for selecting an
appropriate tool for a given task, consider the main steps in building
an intelligent system and discuss how to turn data into knowledge.
The process of building an intelligent system begins with gaining an understanding
of the problem domain. We first must assess the problem and
determine what data are available and what is needed to solve the problem.
Once the problem is understood, we can choose an appropriate tool and develop
the system with this tool. The process of building intelligent knowledge-based
systems is called knowledge engineering. It has six basic phases (Waterman,
1986; Durkin, 1994):
1 Problem assessment
2 Data and knowledge acquisition
3 Development of a prototype system
4 Development of a complete system
5 Evaluation and revision of the system
6 Integration and maintenance of the system