In expert systems, expert system shells are the software that contains an interface, an inference engine, and the formatted skeleton of a knowledge base. In essence, an expert system shell is an empty container to be filled with the elements of expert knowledge that the inference engine can process for users. Expert systems are computer applications that provide specific problem-solving help that a user may need to access to resolve, for example, a difficulty operating utility software. A knowledge engineer would use this shell to develop the knowledge base and customize it to meet the needs of their specific customer base of users. It would be customized to take input from a user and interpret that information for the data repository and, by comparison, find corresponding information that can help guide the user to a solution.
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Along with the control information that is deposited in a knowledge base are rules and attribute definitions that govern the release of information to users. The knowledge base is built from specialization statements that mimic the process of analyzing a human expert in search of sufficient knowledge to reach a solution. Expert system shells must provide capabilities to support the knowledge engineer’s work in developing a knowledge base that can operate as a real-time expert system. In such an expert system, the basis may be constantly changing data by deletions or additions of data because industrial systems, networks, hardware, and software systems change over time. This constant change in input from other management systems should not hamper the base’s ability to reason at the same expert level, regardless of changes.
Expert systems shells provide the skeleton for imitating expert human reasoning in rule methods known as forward chaining and reverse chaining. Direct chaining in these shells allows you to get data from a user and use inference engine rules to find more data pertaining to that information until there is enough information to form a conclusion. Since the initial data received is what drives the search, this method is called a data-driven method. An application that illustrates this direct chaining method can explore the possibilities of organizing components within a computer to arrive at the best placement of components.
Reverse threading gathers data only when it needs it when a knowledge base is being queried in a query. It aims to find a value for C and reasons backwards to find the value of A and B that conclude the objective value of C. This method of reasoning from current data to previous data that was the basis of the present data is called objective-directed method. An application that illustrates expert system shell inference rules might include a physician entering a current set of symptoms for background information about the same or similar symptoms into background information for a specific medical diagnostic expert system.
Inferred knowledge is obtained by examining existing facts to arrive at likely new information. This is the reasoning process that inhabits the inference engine in expert system shells. This process is what initiates forward or backward chaining in rule-based expert systems. The inference rules that build the inference engines in expert system shells are made up of conditional “if” clauses and “then” clauses in rule declarations that facilitate step guidance. These steps can be in the areas of financial services, human resources, and mortgage loan handling, among others, to try to figure out the rules of thumb as likely recommendations when a definitive answer is not possible.