Expert Systems: The Past, Present and Future of Knowledge-based Systems :: Exploratory Essays Research Papers

Expert Systems: The Past, Present and Future of Knowledge-based Systems Expert Systems were invented as a way to decrease the reliance by corporations on human "experts" -- people who apply reasoning and experience to make judgements in a specific field, such as medicine, insurance underwriting or the operation of a power-plant. Hence, an expert system should include a database of facts and a way of reasoning about them. In many, but not all, applications it is also helpful to have a way for the system to reason with probabilities or non-Boolean truth values. Expert systems are also sometimes referred to a "knowledge-based systems". In classical AI many different reasoning methods have been tried. A few of the common ones are "forward chaining", in which conclusions are drawn from a set of facts by using modus ponens, syllogism, and other simple tools of logic; "backward chaining", which uses trickier logic, such as modus tollens; and neural nets. Most expert systems simply use forward chaining and backward chaining, with some non-Boolean component such as Fuzzy Logic only where necessary. Expert systems tend to be more practical than AI in general. It is quite possible to build an expert system in a conventional programming-language, such as COBOL, C or Java. However, much of the machinery inside an expert system can be abstracted away from any specific domain, and the main criterion in the success of an expert system is its ease of use and maintenance, not it's ability to make decisions in a fraction of a second. Therefore, it is possible to build a "knowledge system shell" which can then be prepared for almost any domain simply by listing rules and data in a standard form. Few expert systems are written in LISP, because most LISP implementations lack robust user-friendly input-output routines. A good knowledge system shell includes I/O routines, a way to accurately and generally represent facts, and an easy, efficient, accurate way to give the system inference-rules. However, even the best expert system shell is limited by the problem domain to which it is applied. One researcher divided problem domains into four categories: "Class 1. ... if the effective domain decompositions are not known and the available domain knowledge is limited to the set of allowable actions and constraints. An example of such a problem is maze traversal, where the knowledge about the structure of the maze is not available a priori. "Class 2.