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‘Any problem that can be solved by your in-house expert in a 10–30 minute phone call can be developed as an expert system

This is basically very good chapter: what tools that we can choose

Classic, frame representation system

Fuzzy logic is based on the idea that all things admit of degrees

Uncertainty: One of the common characteristics of the information available to human experts is its imperfection. Information can be incomplete, inconsistent, uncertain, or all three. In other words, information is often unsuitable for solving a problem.

Knowledge engineering is the process of building intelligent knwoledge-based systems. There are six main steps: assess the problem; acquire data and knowledge; develop a prototype system; develop a complete system; evaluate and revise the system; and integrate and maintain the system.

The Gini coefficient is, essentially, a measure of how well the predictor separates the classes contained in the parent node.

The combination of fuzzy logic and neural networks constitutes a powerful means for designing intelligent systems. Domain knowledge can be put into a neuro-fuzzy system by human experts in the form of linguistic variables and fuzzy rules. When a representative set of examples is available, a neuro-fuzzy system can automatically transform it into a robust set of fuzzy IF-THEN rules...

Solving a problem by genetic programming involves determining the set of arguments, selecting the set of functions, defining a fitness function to evaluate the performance of created computer programs, and choosing the method for designating a result of the run.

frames are used to represent knowledge in a frame-based expert system. A frame contains knowledge of a given object, including its name and a set of attributes also called slots. Name, weight, height and age are attributes of a frame. Model, processor, memory and prize are attributes of the frame Computer.

Decomposition of a problem into frames, frames into slots and facets depends on the nature of the problem itself and the judgement of the knowledge engineer. There is no predefined 'correct' representation.

To improve the computational efficiency of fuzzy interference, Sugeno used a single spike, a singleton, as the membership function of the rule consequent. The Sugeno method works well with optimisation and adaptive techniques, which makes it very attractive in control, particularly for dynamic nonlinear systems.

Fuzzy logic is determined as a set of mathematical principles for knowledge representation based on degrees of membership rather than on crisp membership of classical binary logic.