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Forward Chaining

Forward Chaining. DFS can supply compatible bindings for forward chaining. Forward Chaining. Until no rule produces an assertion or the animal is identified For each rule Try to support each of the rule’s antecedents by matching it to known assertions

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Forward Chaining

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  1. Forward Chaining • DFS can supply compatible bindings for forward chaining

  2. Forward Chaining • Until no rule produces an assertion or the animal is identified • For each rule • Try to support each of the rule’s antecedents by matching it to known assertions • If all the rule’s antecedents are supported, assert the consequent unless there is an identical assertion already • Repeat for all matching and instantiation alternatives

  3. Forward Chaining • Until no rule produces a new assertion • For each rule • For each set of possible variable bindings determined by matching the antecedents to working memory • Instantiate the consequent • Determine whether the instantiated consequent is already asserted. If it is not, assert it

  4. Working Memory

  5. Rule • If • ?x is-a horse • ?x is-a-parent-of ?y • ?y is fast • Then • ?x is valuable

  6. Find Bindings • If ?x is-a horse • ?x can be bound to • Comet, Prancer, Thunder, Dasher • ?x is-a-parent-of ?y • … • ?y is fast • …

  7. Backward Chaining • Start matching hypothesis against • Existing assertions • Rule consequents • Comet is valuable?

  8. Backward chaining code • Until all hypothesis have been tried and none have been supported or until the animal has been identified • For each hypothesis • For each rule whose consequent matches the current hypothesis • Try to support each of the rule’s antecedents by matching it to assertions in WM or by backward chaining through another rule, creating new hypotheses. Be sure to check all matching and instantiating alternatives • If all the rule’s antecedents are supported, announce success and conclude that the hypothesis is true

  9. Rules and Cognition • Simplicity of rule based systems enables you to build extremely useful applications • RBS can explain their reasoning • How did you establish swifty was a cheetah? By using rule six and by knowing that swifty is a mammal, has pointed teeth, has claws, and has forward pointing eyes. • How…? : Report the assertions connected to the rule’s antecedents. The rule that established the assertion.

  10. Rules and Cognition • RBS can explain their reasoning • Why did you show that swifty is a mammal? Because I wanted to use rule 6 to show that the animal is a carnivore • Why..? Report the assertions to the then sides of all rules that used the assertion referenced in the question.

  11. Knowledge Acquisition • Two key heuristics • Work with specific situations • Ask about situation pairs that look identical but are handled differently

  12. RBS behave like idiot savants • They do certain tasks well • But • They do not reason at multiple levels • They do not understand the reasoning behind their rules • They do not know how or when to break their own rules • They do not look at problems from different perspectives

  13. SOAR models human problem solving - MAYBE • Uses an automatic preference analyzer instead of a fixed conflict resolution strategy

  14. RBS vs C/Java/Lisp • In principle there is nothing to prevent building a more human like system using rules, because rules can be used as a sort of programming language • However, compared to other programming languages rules have little to offer

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