L4rulebasedsystems1(ch2)

L4rulebasedsystems1(ch2) - Production Systems Rule base...

Info iconThis preview shows pages 1–9. Sign up to view the full content.

View Full Document Right Arrow Icon
Production Systems Rule base Systems (Book and Busse book handout) CSE 352 Lecture Notes (4) Professor Anita Wasilewska
Background image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Production Systems (Rule Based Systems) A production system consists of: 1.A knowledge base , also called a rule base containing production rules , or productions. 2.A database , contains facts 3.A rule interpreter , also called a rule application module to control the entire production system.
Background image of page 2
Production Rules (Expert System Rules) Production rules are the units of knowledge of the form: IF conditions THEN actions Condition part of the rule is also called the IF part, premise, antecedent or left side of the rule.
Background image of page 3

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Production Rules (Expert System Rules) Action part is also called THEN part, conclusion, consequent, succeedent, or the right side of the rule. Actions are executed when conditions are true and the rule is fired . Rules Format: C 1 2 n => A C 1 , … , C n , A are atomic formulas
Background image of page 4
Production Rule (Expert System Rule) 1. Propositional logic conceptualization: rules are propositional logic formulas i.e. Rules are: C 1 2 n => A where C 1 , … , C n , A are atomic formulas In this case atomic formulas are propositional variables or (sometimes) their negations. All our book examples use propositional logic conceptualization !
Background image of page 5

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Production Rule (Expert System Rule) 2. Predicate logic conceptualization (knowledge representation) Rules are: C 1 2 n => A where C 1 , … , C n , A are atomic formulas Atomic formulas now represent records in the database and are written in a triple form: (x, attribute, value of the attribute) or in a predicate form attribute (x, value of the attribute )
Background image of page 6
Production System ES ES = (R, RI, DBF) R - is a finite set of production rules RI – is an inference engine called rule interpreter DBF – is a database of facts (changing dynamically) Rules are always C 1 n => A For n> = 1 and C 1 , C n , A are atomic formulas
Background image of page 7

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Propositional Rule of Inference in ES Rules Interpreter RI Rule of inference of the Rule Interpreter is: C 1 2 n => A ; C 1 , …C n A for C 1 , …C n belonging to DBF APPLICATION of the Rule of Inference means that for a given rule of the production ( expert) system ES C 1 n -> A the rule interpreter RI will check database of facts DBF and if all C 1 ,…,C n belong to
Background image of page 8
Image of page 9
This is the end of the preview. Sign up to access the rest of the document.

This note was uploaded on 01/25/2012 for the course CSE 352 taught by Professor Wasilewska,a during the Fall '08 term at SUNY Stony Brook.

Page1 / 24

L4rulebasedsystems1(ch2) - Production Systems Rule base...

This preview shows document pages 1 - 9. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online