Lesson 17 - Module 7 Knowledge Representation and Logic...

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Module 7 Knowledge Representation and Logic – (Rule based Systems) Version 1 CSE IIT, Kharagpur
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7.1 Instructional Objective The students should understand the use of rules as a restricted class of first order logic statements The student should be familiar with the concept of Horn clause Students should be able to understand and implement the following reasoning algorithm o Forward chaining o Backward chaining Students should understand the nature of the PROLOG programming language and the reasoning method used in it. Students should be able to write elementary PROLOG programs Students should understand the architecture and organization of expert systems and issues involved in designing an expert system At the end of this lesson the student should be able to do the following: Represent a knowledge base as a set of rules if possible Apply forward/backward chaining algorithm as suitable Write elementary programs in PROLOG Design expert systems Version 1 CSE IIT, Kharagpur
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Lesson 17 Rule based Systems - I Version 1 CSE IIT, Kharagpur
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7.2 Rule Based Systems Instead of representing knowledge in a relatively declarative, static way (as a bunch of things that are true), rule-based system represent knowledge in terms of a bunch of rules that tell you what you should do or what you could conclude in different situations. A rule-based system consists of a bunch of IF-THEN rules, a bunch of facts, and some interpreter controlling the application of the rules, given the facts. Hence, this are also sometimes referred to as production systems. Such rules can be represented using Horn clause logic. There are two broad kinds of rule system: forward chaining systems, and backward chaining systems. In a forward chaining system you start with the initial facts, and keep using the rules to draw new conclusions (or take certain actions) given those facts. In a backward chaining system you start with some hypothesis (or goal) you are trying to prove, and keep looking for rules that would allow you to conclude that hypothesis, perhaps setting new subgoals to prove as you go. Forward chaining systems are primarily data-driven, while backward chaining systems are goal-driven. We'll look at both, and when each might be useful. 7.2.1 Horn Clause Logic There is an important special case where inference can be made substantially more focused than in the case of general resolution. This is the case where all the clauses are Horn clauses. Definition: A Horn clause is a clause with at most one positive literal. Any Horn clause therefore belongs to one of four categories: A rule : 1 positive literal, at least 1 negative literal. A rule has the form "~P1 V ~P2 V . .. V ~Pk V Q". This is logically equivalent to "[P1^P2^ . .. ^Pk] => Q"; thus, an if-then implication with any number of conditions but one conclusion. Examples: "~man(X) V mortal(X)" (All men are mortal); "~parent(X,Y) V
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This note was uploaded on 09/20/2010 for the course MCA DEPART 501 taught by Professor Hemant during the Fall '10 term at Institute of Computer Technology College.

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Lesson 17 - Module 7 Knowledge Representation and Logic...

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