Encyclopedia of Computer Science and Technology

188 expert systemsAnatomy of an Expert SystemAn expert system has two main components, a knowledge baseand an inference engine. The knowledge base consists of a setof assertions (facts) or of rules expressed as if . . . then statementsthat specify conditions that, if true, allow a particularinference to be drawn (see prolog). The inference engineaccepts new assertions or queries and tests them against thestored rules. Because satisfying one rule can create a conditionthat is to be tested by a subsequent rule, chains of reasoningcan be built up. If the reasoning is from initial factsto an ultimate conclusion, it is called forward chaining. If aconclusion is given and the goal is to prove that conclusion,there can be backward chaining from the conclusion to theassertions (similar to axioms in mathematical proofs).While some rules are ironclad (for example, if a closedstraight figure has three sides, it’s a triangle) in many realworldapplications it is necessary to take a probabilisticapproach. For example, experience might suggest that if acustomer buys reference books there is a 40 percent chancethe customer will also buy a related CD-ROM product.Thus, rules can be given weights or confidence factors and asthe rules are chained, a cumulative probability for the conclusioncan be generated and some threshold probabilityfor asserting a conclusion can be specified. (See also fuzzylogic and uncertainty).While rules-based inference systems are relatively easyto traverse automatically, they may lack the flexibility tocodify the knowledge needed for complex activities (such asautomatic analysis of news stories). An alternative approachinvolves the construction of a knowledge base consistingof frames. A frame (also called a schema) is an encodeddescription of the characteristics and relationships of entities.For example, an expert system designed to analyzecourt cases might have frames that describe the roles andinterests of the defendant, defense counsel, prosecutor, andso on, and other frames describing the trial and sentencingprocess. Using this knowledge, the system might be able topredict what sort of plea agreement a particular defendantmight reach with the state. While potentially more robustthan a rules-based system, a frames-based system faces thetwin challenges of building and maintaining a complex andopen-ended knowledge base and of developing methods ofreasoning more akin to generalized artificial intelligence(see artificial intelligence).Building an expert system requires that the knowledge of expertsbe “captured” in the form of a series of assertions and rules calleda knowledge base. Once the knowledge base is established, usersseeking advice can use an inference engine to examine the knowledgebase for valid conclusions that can be expressed as recommendations,often with varying degrees of confidence.TrendsExpert systems (particularly of the rules-based variety) nowhave an established place in business, industry, and science.The field of genomics and genetic engineering, widelyseen as the “technology of the 21st century” may be a particularlyfruitful applications area for analytical expert systems.Another promising area is the use of expert systemsfor e-commerce marketing analysis (see data mining). Anemerging emphasis in expert system development is theuse of object-oriented concepts (see object-oriented programming)and distributed database and knowledge sharingtechnology to build and maintain large knowledge basesmore efficiently.Further Reading“Expert Systems.” American Association for Artificial Intelligence.Available online. URL: http://www.aaai.org/AITopics/html/expert.html. Accessed July 31, 2007.Giarrartano, Joseph C., and Gary D. Riley. Expert Systems: Principlesand Programming. 4th ed. Boston: Thomson CourseTechnology, 2004.“Introduction to Expert Systems.” Available online. URL: http://www.expertise2go.com/webesie/tutorials/ESIntro/. AccessedJuly 31, 2007.Jackson, Peter. Introduction to Expert Systems. 3rd ed. Reading,Mass.: Addison-Wesley, 1998.