Lecture 2 Rule-based expert systems ■ Introduction, or what is knowledge? ■ Rules as a knowledge representation technique ■ The main players in the development team ■ Structure of a rule-based expert system ■ Characteristics of an expert system ■ Forward chaining and backward chaining ■ Conflict resolution ■ Summary  Negnevitsky, Pearson Education, 2002 1 Introduction, or what is knowledge? ■ Knowledge is a theoretical or practical understanding of a subject or a domain. Knowledge is also the sum of what is currently known, and apparently knowledge is power. Those who possess knowledge are called experts. ■ Anyone can be considered a domain expert if he or she has deep knowledge (of both facts and rules) and strong practical experience in a particular domain. The area of the domain may be limited. In general, an expert is a skilful person who can do things other people cannot.  Negnevitsky, Pearson Education, 2002 2 ■ The human mental process is internal, and it is too complex to be represented as an algorithm. However, most experts are capable of expressing their knowledge in the form of rules for problem solving. IF THEN the ‘traffic light’ is green the action is go IF THEN the ‘traffic light’ is red the action is stop  Negnevitsky, Pearson Education, 2002 3 Rules as a knowledge representation technique ■ The term rule in AI, which is the most commonly used type of knowledge representation, can be defined as an IF-THEN structure that relates given information or facts in the IF part to some action in the THEN part. A rule provides some description of how to solve a problem. Rules are relatively easy to create and understand. ■ Any rule consists of two parts: the IF part, called the antecedent (premise or condition) and the THEN part called the consequent (conclusion or action).  Negnevitsky, Pearson Education, 2002 4 IF THEN <antecedent> <consequent> ■ A rule can have multiple antecedents joined by the keywords AND (conjunction), OR (disjunct

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