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

unspoilt Systems The Past, Present and Future of Knowledge-based SystemsExpert Systems were invented as a bearing to decr backup man the reliance by corporations on human experts -- people who devote reasoning and experience to make judgements in a detail field, such as medicine, insurance underwriting or the operation of a power-plant. Hence, an expert carcass should include a database of facts and a port of reasoning about them. In many, but not all, applications it is alike helpful to have a way for the carcass to reason with probabilities or non-Boolean truth values. Expert systems are also nightimes referred to a friendship-based systems.In classical AI many variant reasoning methods have been tried. A few of the common ones are onward chaining, in which conclusions are drawn from a set of facts by victimisation modus ponens, syllogism, and other simple tools of logic backward chaining, which uses trickier logic, such as modus tollens and skittish nets. Most exp ert 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 stodgy programming-language, such as COBOL, C or Java. However, much of the machinery inside an expert system can be abstracted away from any specific humankind, 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 figure of a second. Therefore, it is possible to build a knowledge system compositors case which can then be prepared for almost any field of operations simply by listing rules and data in a measuring form. Few expert systems are written in say, 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 mainly represent facts, and an easy, ef ficient, accurate way to give the system inference-rules. However, even the silk hat expert system shell is limited by the line of work domain to which it is applied. One researcher divided problem domains into four categoriesClass 1. ... if the legal domain decompositions are not known and the addressable 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.