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Forward Chaining Systems

Forward Chaining Systems. also known as Production Systems commonly used for Expert Systems knowledge based must consist of facts and rules (Horn clauses, extended with retraction) will describe the Rete Algorithm representation of concepts as network or graph. Rete Algorithm.

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Forward Chaining Systems

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  1. Forward Chaining Systems • also known as Production Systems • commonly used for Expert Systems • knowledge based must consist of facts and rules (Horn clauses, extended with retraction) • will describe the Rete Algorithm • representation of concepts as network or graph

  2. Rete Algorithm • create a network where nodes represent ground literals (predicates with concrete arguments) • rules link antecedents to consequent • rules can create new nodes • start by activating nodes corresponding to facts • each iteration, determine which rules can fire • pick one and modify network • run until quiescence • produces all the consequences of the facts

  3. Example Rete Network

  4. Conflict Resolution • What happens when 2 rules can fire that have opposite effects? e.g assert(P) and retract(P)? • assign numeric priorities to rules – highest wins • can retract antecedents of other rules

  5. (defrule library-rule-1 (book (name ?X) (status late) (borrower ?Y)) (borrower (name ?Y) (address ?Z)) => (send-late-notice ?X ?Y ?Z)) Examples in JESS (deffacts trouble_shooting (car_problem (name ignition_key) (status on)) (car_problem (name engine) (status wont_start)) (car_problem (name headlights) (status work)) ) (defrule rule1 (car_problem (name ignition_key) (status on)) (car_problem (name engine) (status wont_start) (car_problem (name battery) (status OK)) => assert (car_problem (name starter) (status faulty))) ) (defrule rule2 (car_problem (name headlights) (status work)) => assert (car_problem (name battery) (status OK)) ) ignition_key: on starter: faulty engine: wont_start battery: OK headlights: work

  6. Implementations • ACT, SOAR – cognitive models, simulate retrieval from long-term/short-term memory, activation by association, activation decay • CLIPS – C-based developed at NASA • JESS – Java-based, popular • Subsumption Architecture (Rodney Brooks) • intelligent behavior in robots can be produced in a decentralized way by a lot simple rules interacting • divide behaviors into lower-level basic survival behaviors that have higher priority, and higher-level goal-directed behaviors • example: 6-legged robot ants learning to walk

  7. Description Logics • Theorem: entailment FOL is undecidable • theorem provers like resolution might take a long time to find a proof • Goal: knowledge representation system in which inference is more efficient than FOL • solution: restrict expressiveness (e.g. eliminate disjunction and negation) • remember: proofs with Horn-clauses are linear-time • will describe several DL systems • see Ch. 1 of Handbook of Description Logics for overview, link posted on web • focus on defining taxonomy of concepts • currently popular for web applications (“Semantic Web”)

  8. Concept Description Syntax • see fig 12.7 (p. 456) in book • T-box – terminological, concept definitions Concept ::= Thing | ConceptName | AND(Concept...) | All(RoleName,Concept) | AtLeast(Int,RoleName) | AtMost(Int,RoleName) | Fills(RoleName,Individual) | OneOf(Individual...) • Bachelor = AND(Adult,Male,Unmarried) • MotorCycle = AND(vehicle,AtMost(2,wheels)) • HonorStudent  ALL(Fills(Grade,A),Fills(ClassTaken,Class)) • A-box – assertional, describe individuals • Male(bob), Fills(sister(bob),sue)

  9. Family of DL languages • described in Ch. 2 of Handbook of DL AL family a person who has at most one child, or 3 or more children including a daughter ALCN: full existential quantification full negation full number restriction

  10. Examples

  11. Inference • subsumption: is CD? • reduce to satisfiability • C∩D? •  is the “bottom” or NULL concept, nothing is in this set • classification: find all subset relationships in hierarchy • tableau algorithms • make model by expanding and re-writing concept definitions, or detect failure • PSPACE-complete for ALCN

  12. Semantic Web • OWL - Web Ontology • extend data in XML with inference rules (written in RDF) • SHOIQN semantics • for example, if web page A is annotated with <student> <name>Joe Smith</name> <advisor>Dr. Hank Walker</advisor> <institution>TAMU</institution> </student> • then should be able to infer that Dr. Walker is a faculty member at TAMU

  13. Implementations • old: CLASSIC, KL-ONE, LOOM • modern: Protege • Applications • medical records • SNOMED ontology – describes terms for symptoms, diseases, procedures, anatomy, etc. • Dublin Core • for information retrieval/media archiving • describes books, journals, thesis, authors, publishers, ISBN, revisions, affiliations, conferences...

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