Forward Chaining Systems

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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

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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

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Example Rete Network
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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

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Examples in JESS
(defrule library-rule-1 (book (name ?X)
(status late) (borrower ?Y)) (borrower (name
?Y) (address ?Z)) gt (send-late-notice ?X
?Y ?Z))

• (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))
• gt
• assert (car_problem (name starter) (status
  faulty))) )
• (defrule rule2
• (car_problem (name headlights) (status work))
• gt
• assert (car_problem (name battery) (status
  OK)) )

ignition_key on
starter faulty
engine wont_start
battery OK
headlights work
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• 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

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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)

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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(ClassTake
  n,Class))
• A-box assertional, describe individuals
• Male(bob), Fills(sister(bob),sue)

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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
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Examples
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Inference

• subsumption is C?D?
• reduce to satisfiability
• Cn?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

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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
• ltstudentgt
• ltnamegtJoe Smithlt/namegt
• ltadvisorgtDr. Hank Walkerlt/advisorgt
• ltinstitutiongtTAMUlt/institutiongt
• lt/studentgt
• then should be able to infer that Dr. Walker is a
  faculty member at TAMU

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• 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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