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Lecture 04 Rule Representation

Lecture 04 Rule Representation. Topics Rules as a Knowledge Representation Technique Rule Inference Model Characteristics of Rule Representation Forward Chaining Backward Chaining Discussion. Rules as a representation technique.

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Lecture 04 Rule Representation

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  1. Lecture 04 Rule Representation • Topics • Rules as a Knowledge Representation Technique • Rule Inference Model • Characteristics of Rule Representation • Forward Chaining • Backward Chaining • Discussion

  2. Rules as a representation technique • Experts are capable of expressing their knowledge in the form of rules for problem solving • IF the ‘traffic light’ is green THEN the action is go • IF the ‘traffic light’ is red THEN the action is stop • 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)

  3. Rules as a representation technique • A rule can have multiple antecedents joined by the keywords AND (conjunction), OR (disjunction) or a combination of both • IF antecedent 1 IF antecedent 1 AND antecedent 2 OR antecedent 2 : : AND antecedent n OR antecedent n THEN consequent THENconsequent

  4. Rules as a representation technique • Rules can represent relations, recommendations, directives, strategies and heuristics • Relation IF the ‘fuel tank’ is empty THEN the car is dead • Recommendation IF the season is autumn AND the sky is cloudy AND the forecast is drizzle HEN the advice is ‘take an umbrella’ • Directive IF the car is dead AND the ‘fuel tank’ is empty THEN the action is ‘refuel the car’

  5. Rules as a representation technique • Strategy IF the car is dead THEN the action is ‘check the fuel tank’; step1 is complete IF step1 is complete AND the ‘fuel tank’ is full THEN the action is ‘check the battery’; step2 is complete • Heuristic IF the spill is liquid AND the ‘spill pH’ < 6 AND the ‘spill smell’ is vinegar THEN the ‘spill material’ is ‘acetic acid’

  6. Rule inference model • Rule-based inference architecture External Database External Programs Rule Base Fact base Inference Engine User Interface User

  7. Rule inference model • Rule base contains rules about task knowledge • Fact base contains facts about the current situation; Facts are used to match against the IF (condition) parts of rules • External programs and database • Inference engine carries out the reasoning by linking the rules with the facts. When the condition part of a rule is satisfied, the rule is said enabled and the action part is executed (fired) • User interface solicits problem specification and displays the inference results

  8. Rule inference model • Match-fire cycle • Recognize enabled rules by match • Resolve conflicting rules • Fire rules and add new facts back to fact base

  9. Conflict resolution • Suppose we have two rules with the same IF part. Both of them can be enabled when the condition part is satisfied. They represent a conflict set. • The inference engine must determine which rule to fire from such a set. • A method for choosing a rule to fire when more than one rule can be fired in a given cycle is called conflict resolution.

  10. Conflict resolution • Resolution strategies • Priority • Specificity • Recency • Meta-knowledge • Meta-rule 1: Rules supplied by experts have higher priorities than rules supplied by novices • Metarule 2:Rules governing the rescue of human lives have higher priorities than rules concerned with clearing overloads on power system equipment.

  11. Characteristics of rule representation • Represent knowledge to guide the reasoning and thus reduce the search space for a solution • Rules are separated from its processing; knowledge base and the inference engine are split up • Inexact reasoning is possible with confidence degree associated (to be discussed in next lecture) • Rule base maintenance is, in general, easier owing to local modification by adding single rules

  12. Forward chaining • Data-driven reasoning

  13. Forward chaining • Forward chaining is a technique for gathering information and then inferring from it whatever can be inferred • Many rules may be executed that have nothing to do with the established goal • If our goal is to infer only one particular fact, the forward chaining inference technique would not be efficient

  14. Backward chaining • Goal-driven reasoning

  15. Backward chaining • If one begins with a hypothetical solution and then attempts to find facts to prove it, choose the backward chaining inference engine

  16. Discussion • Rules vs. First-order definite clause • In rule: IF the season is autumn AND the sky is cloudy AND the forecast is drizzle THEN the advice is ‘take an umbrella’ • In First-order definite clause Autumn(Season) Cloudy(Sky)  Drizzle(Forecast) Umbrella(Advice) • In First-order logic Autumn(Season)  Cloudy(Sky)  Drizzle(Forecast) →Umbrella(Advice)

  17. Discussion • Rule inference • Modus ponens ├mp • {c1c2…cn→a, c1’c2’…cn’} ├mps(, a) •  = Unify(c1c2…cn, c1’c2’…cn’) • ├mp is sound and complete on First-order definite clause (also Horn clause)

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