Chapter 12Expert Systems Design Examples.ppt

Expert Systems: Principles and Programming, Fourth Edition Chapter 12:Expert Systems Design Examples Expert Systems: Principles and Programming, Fourth Edition Summary Learn about incorporating uncertainty into CLIPS The use of decision trees to provide a useful paradigm Learn how to emulate backward chaining in a CLIPS program Constructing a framework of simpler expert systems into one large program Uncertainty Factors Although CLIPS has no built-in capabilities for handling uncertainty, it is possible to incorporate uncertainty into CLIPS by placing information dealing with uncertainty directly into the facts and rules. We begin by exploring how uncertainty was incorporated into the expert system MYCIN. MYCIN and Uncertainty MYCIN represents factual information as object-attribute-value (OAV) triplets. MYCIN also associates with each fact a certainty factor (CF) which represents a degree of belief in the fact. -1 means the fact is false 0 means no information is known about the fact 1 means the fact is known to be true MYCIN and Uncertainty Because CLIPS does not handle uncertainty factors automatically, a slot in each fact will be used to represent the uncertainty factor. MYCIN allows the same OAV triple to be derived by separate rules. The OAV triples are then combined producing a single OAV triple that combines the certainty factors. MYCIN and Uncertainty In order to allow identical OAV triples to be asserted with the same certainty factors, the set-fact-duplication command can be used to disable the CLIPS behavior preventing duplicated facts from being asserted. MYCIN combines two identical OAV triples into a single OAV triple with a combined uncertainty, computed as: New Uncertainty = (CF1 + CF2) – (CF1 * CF2) MYCIN and Uncertainty Since CLIPS does not automatically handle certainty factors for facts, it does not automatically combine two OAV triplets derived from different rules. The combination is handled by a rule that searches the fact list for iden