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 SystemsExpert Systems were invented as a way to decrease the reliance by corporations on human in effect(p)s -- citizenry who apply reasoning and experience to make judgements in a specialised field, such as medicine, insurance underwriting or the operation of a power-plant. Hence, an expert administration should include a database of facts and a way of reasoning intimately them. In some, 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 argon also sometimes referred to a acquaintance-based systems.In classical AI m both different reasoning methods have been tried. A few of the common ones atomic number 18 forward chaining, in which conclusions are drawn from a set of facts by using modus ponens, syllogism, and other simple tools of logic retroflexed chaining, which uses trickier logic, such as modus tollens and n eural nets. Most expert systems simply use forward chaining and backward chaining, with some non-Boolean component such as Fuzzy Logic exclusively 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 at heart an expert system can be abstracted away from any specific universe, and the main criterion in the success of an expert system is its ease of use and maintenance, not its 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, eve the best expert system shell is limited by the hassle domain to which it is applied. One researcher divided problem domains into four categories descriptor 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.