Expert Systems
An expert system is an example of a Knowledge Based System. Knowledge Based Systems do not just store data, but also the rules that can be used to manipulate that data to answer questions about it. This knowledge consists not only of sets of rules about how to manipulate different kinds of data but uses methods for representing knowledge and enables acquisition and integration of new knowledge. Knowledge representation and processing information about the world is a major concern for developing expert systems. Pattern Recognition and the ability to learn are key characteristics of intelligent systems. An expert system provides expert advice (decisions, recommendations or solutions) replacing a real person. In an expert system, the program incorporates the knowledge of an expert in a particular field. These systems should capture and deliver knowledge that is not easily represented using traditional computing approaches.
Pattern Recognition plays an important role in expert systems and there is growing interaction between expert systems and pattern analysis. Core elements of Pattern Recognition, including "learning techniques" and "inference" play an important and central role in artificial intelligence; visual perception, scene analysis, and image understanding are essential to robotic vision. Methods such as knowledge representation, semantic networks, and heuristic searching algorithms can also be applied to improve the pattern representation and matching techniques for so-called "smart" Pattern Recognition. Problem solving expertise needs memory, knowledgebase schemas and representation. The transfer of decision making and problem solving to machines has a high economic potential and the necessity of finding models for problem solution has also initiated research in the question of how humans "do it". The goal of cognitive psychology is to understand the nature of human intelligence and how it works.