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Expert system shells are software with an interface, inference engine, and formatted knowledge base skeleton. They assist in solving specific problems by mimicking human reasoning and can be customized to meet user needs. Inference rules guide forward or backward chaining to arrive at probable new information.
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 bowl to be filled with the elements of expert knowledge that the inference engine can process for users. Expert systems are computer applications that provide assistance in solving specific problems that a user might need access to to resolve, for example, a utility software malfunction. A knowledge engineer would use this shell to develop the knowledge base and customize it to meet the needs of his or her particular user base. It would be customized to take a user’s input and interpret that information in the data repository and, by comparison, locate the corresponding information that could help guide the user towards a solution.
Along with the control information that is deposited in a knowledge base, there are rules and attribute definitions that govern the release of the information to users. The knowledge base consists of proficiency statements that mimic the analysis process of a human expert looking for sufficient knowledge to obtain a solution. Expert system shells must provide capabilities to strengthen the knowledge engineer’s work in developing a knowledge base that can function as a real-time expert system. In such an expert system, the basis can be constantly changing data by deletions or additions of data because industrial systems, networks, hardware and software systems change over time. This constant change of data input from other management systems must not falter the ability of the grassroots to reason at the same level as experts, regardless of changes.
Expert systems shells provide the bare bones for imitating human reasoning proficient in the rules methods known as forward chaining and backward chaining. Forward chaining in these shells allows you to take data from a user and use inference engine rules to locate more data about 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 demonstrating this forward chaining method could explore the possibilities of arranging components within a computer to arrive at the best component placement.
Backward chaining collects data only as it needs it when a knowledge base is queried about a query. It has an objective to find a value for C and thinks backwards to find out the value of A and B which conclude the objective value of C. This method of reasoning from present data to previous data which was the basis of present data is called objective – guided method. An application demonstrating expert systems shell inference rules might include a physician entering a current set of symptoms for background information on the same or similar symptoms into background information from a particular medical diagnosis expert system.
Inferred knowledge is acquired by examining existing facts to arrive at probable new information. This is the reasoning process that inhabits the inference engine in expert system shells. This process is what initiates forward or reverse chaining in rule-based expert systems. The inference rules that build inference engines in expert system shells consist of conditional “if” clauses and “then” clauses in ruling statements that help guide steps. These steps could be in the fields of financial services, human resources, and mortgage loan management, among others, to try to uncover rules of thumb as likely recommendations when a definitive answer isn’t possible.
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