Introduction
"Artificial Intelligence" (AI) was first coined to describe a specific set of computer science problems. Today a popular area within AI is referred to as, knowledge-based systems .

Knowledge Based Systems
Knowledge-based systems are characterized in various levels of performance such as: expert, colleague and assistant systems. The expert (knowledge-based) system is a man-machine system where the machine performs at a level of expertise much higher than its human user. In a colleague system , the machine's performance is similar to that of its human user, with both human and machine sharing the responsibility for decisions. Typically, an assistant system is only a decision aid performing as a data source. Clarifying and reproducing knowledge is the central task in building expert systems.

Expert System Development
There are several stages in the evolution of the development of an expert system which Netmation can assist in some or all of the steps.

Identification Determining problem characteristics Conceptualization Finding concepts to represent knowledge Formalization Designing structures to organize knowledge Implementation Formulating rules that embody knowledge Testing Validating rules that embody knowledge

During conceptualization , the expert and knowledge engineer explicate the key concepts, relations, and information-flow characteristics needed to describe problem-solving process in the given domain. They also specify subtasks, strategies and constraints related to problem-solving.

Formalization involves mapping key concepts and relations into a formal representation suggested by some expert-system-building tool or language. The knowledge engineer must select the language and, with the help of the expert, represent the basic concepts and relations within the language framework.

During implementation, the knowledge engineer combines and reorganizes the formalized knowledge to make it compatible with the information flow characteristics of the problem. The resulting set of rules and associated control structure define a prototype program capable of being executed and tested.

Finally, testing involves evaluation of the performance of the prototype program and revising it to conform to standards of excellence defined by experts in the problem domain. Typically, the expert evaluates the program's performance and assists the knowledge engineer in the forthcoming revisions.

These stages of expert system development are not clear-cut, well- defined, or even independent. At best, they characterize, roughly, the complex process we call knowledge acquisition The knowledge base may reach an unmanageable size and shape. If so, the knowledge engineer and expert reassess categorizations and representations initially selected in an attempt to re-think the basic foundations of their approach or to reconceptualize the expertise. If successful, knowledge is reorganized and a more suitable architecture is selected for the needed reasoning process. The existing systems give way to a new one. Few existing systems have ever undergone more than one rebirth of this sort; however, a very long term development effort may see more of these major paradigm shifts.

In summary, an expert system evolves.