Expert system

Expert systems and decision support systems are software which mimics the inductive or deductive reasoning of a human expert. Complex problems such as diagnosis, perdition, interpretation, planning and design are modeled and solved using inferences from facts and rules derived from expert knowledge. Expert systems may be defined by their task specialization and performance, problem-solving ability in a given domain, ability to reformulate a problem efficiency, complexity or symbol manipulation. Such software is the practical application of artificial intelligence research. Expert systems for management advisory and executive decision support have been deployed in industries such as medical diagnosis, mineral prospecting, military planning, engineering and finance. Typically, the problems to be solved are of the sort that would normally be tackled by a human "expert"—a medical or other professional, in most cases. Real experts in the problem domain are asked to provide "rules of thumb" on how they evaluate the problems, either explicitly with the aid of experienced system developers, or sometimes implicitly, by getting such experts to evaluate test cases and using computer programs to examine the test data and (in a strictly limited manner) derive rules from that. Generally expert systems are used for problems for which there is no single "correct" solution which can be encoded in a conventional algorithm The principal distinction between expert systems and traditional problem solving programs is the way in which the problem related expertise is coded. In traditional applications, problem expertise is encoded in both program and data structures. In the expert system approach all of the problem related expertise is encoded in data structures only; none is in programs. Expert Systems consist of: • Knowledge base (facts) • Production rules ("if..., then..") • Inference engine (controls how "if..., then.." rules are applied towards facts) Actually there are two methods to make conclusions.

	METHOD NAME	SHORT EXPLANATION	USE	EXAMPLE SYSTEMS
Forward chaining	Facts driven	Can find new ideas	CLIPS Jess
Backward chaining	Hypothesis driven	Usually used for diagnosis	Prolog Mycin