SlideShare une entreprise Scribd logo
1  sur  61
Dr. Mustafa Jarrar [email_address]   www.jarrar.info   University of Birzeit Chapter 9 Inference in first-order logic Advanced Artificial Intelligence  (SCOM7341) Lecture Notes  University of Birzeit 2 nd  Semester, 2011
Motivation: Knowledge Bases vs. Databases The DB is a set of tuples, and queries are perfromed truth rvaluation. ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Wffs : Proof xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx Provability Query Answer Knowledge Base System Query Proof Theoretic View Constraints DBMS Transactions i.e insert, update, delete... Query Query Answer Model Theoretic View The KB is a set of formulae and the query evaluation is to prove that the result is provable.
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Inference in First-Order Logic ,[object Object],[object Object],[object Object],[object Object],[object Object]
Propositionalization Universal Quantifiers ,[object Object],[object Object],[object Object],[object Object],[object Object], x  King ( x )     Greedy ( x )     Evil ( x )  King ( John )  Greedy ( John ) ,[object Object],Then we can say So we reduced the FOL  knowledge base into PL KB So we reduced the FOL  sentence into PL sentence
Propositionalization Existential quantifiers ,[object Object],[object Object],[object Object],Crown(C 1 )    OnHead(C 1 ,John)  x Crown(x)    OnHead(x,John) Example: ,[object Object],[object Object],[object Object],provided  C 1  is a new constant symbol, called a  Skolem constant.
Reduction to Propositional Inference ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],irrelevant
Reduction to Propositional Inference ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Reduction to Propositional Inference ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Problems with Propositionalization ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Problems with Propositionalization ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Unification ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Unification Example Unify (p,q)  =  θ   where Subst( θ ,p) = Subset( θ ,q) Suppose we have a query Knows(John,x), we need to unify Knows(John,x) with all sentences in KD. Knows(John,x)  Knows(John,Jane)  Knows(John,x) Knows(y,Bill)  Knows(John,x)  Knows(y,Mother(y)) Knows(John,x) Knows(x,Elizabeth)  Q P   θ
Unification Example {x/Jane} Unify (p,q)  =  θ   where Subst( θ ,p) = Subset( θ ,q) Suppose we have a query Knows(John,x), we need to unify Knows(John,x) with all sentences in KD. Knows(John,x)  Knows(John,Jane)  Knows(John,x) Knows(y,Bill)  Knows(John,x)  Knows(y,Mother(y)) Knows(John,x) Knows(x,Elizabeth)  Q P   θ
Unification Example {x/Jane} {x/Bill,y/John} Unify (p,q)  =  θ   where Subst( θ ,p) = Subset( θ ,q) Suppose we have a query Knows(John,x), we need to unify Knows(John,x) with all sentences in KD. Knows(John,x)  Knows(John,Jane)  Knows(John,x) Knows(y,Bill)  Knows(John,x)  Knows(y,Mother(y)) Knows(John,x) Knows(x,Elizabeth)  Q P   θ
Unification Example {x/Jane} {x/Bill,y/John} {y/John,x/Mother(John)} Unify (p,q)  =  θ   where Subst( θ ,p) = Subset( θ ,q) Suppose we have a query Knows(John,x), we need to unify Knows(John,x) with all sentences in KD. Knows(John,x)  Knows(John,Jane)  Knows(John,x) Knows(y,Bill)  Knows(John,x)  Knows(y,Mother(y)) Knows(John,x) Knows(x,Elizabeth)  Q P   θ
Unification Example {x/Jane} {x/Bill,y/John} {y/John,x/Mother(John)} fail ,[object Object],[object Object],[object Object],Unify (p,q)  =  θ   where Subst( θ ,p) = Subset( θ ,q) Suppose we have a query Knows(John,x), we need to unify Knows(John,x) with all sentences in KD. Knows(John,x)  Knows(John,Jane)  Knows(John,x) Knows(y,Bill)  Knows(John,x)  Knows(y,Mother(y)) Knows(John,x) Knows(x,Elizabeth)  Q P   θ
Unification Example {x/Jane} {x/Bill,y/John} {y/John,x/Mother(John)} {x/Elizabeth, z 17 /John} ,[object Object],[object Object],[object Object],Unify (p,q)  =  θ   where Subst( θ ,p) = Subset( θ ,q) Suppose we have a query Knows(John,x), we need to unify Knows(John,x) with all sentences in KD. Knows(John,x)  Knows(John,Jane)  Knows(John,x) Knows(y,Bill)  Knows(John,x)  Knows(y,Mother(y)) Knows(John,x) Knows(z 17 ,Elizabeth)  Q P   θ
Unification Example {x/Jane} {x/Bill,y/John} {y/John,x/Mother(John)} {x/Elizabeth, z 17 /John} Unify (p,q)  =  θ   where Subst( θ ,p) = Subset( θ ,q) Suppose we have a query Knows(John,x), we need to unify Knows(John,x) with all sentences in KD. Knows(John,x)  Knows(John,Jane)  Knows(John,x) Knows(y,Bill)  Knows(John,x)  Knows(y,Mother(y)) Knows(John,x) Knows(z 17 ,Elizabeth)  Q P   θ
Unification Example {x/Jane} {x/Bill,y/John} {y/John,x/Mother(John)} {x/Elizabeth, z 17 /John} In the last case, we have two answers: θ = {y/John,x/z}, or θ = {y/John,x/John, z/John} ?? This first unification is more general,  as it places fewer restrictions on the  values of the variables. Unify (p,q)  =  θ   where Subst( θ ,p) = Subset( θ ,q) Suppose we have a query Knows(John,x), we need to unify Knows(John,x) with all sentences in KD. Knows(John,x)  Knows(John,Jane)  Knows(John,x) Knows(y,Bill)  Knows(John,x)  Knows(y,Mother(y)) Knows(John,x) Knows(z 17 ,Elizabeth)  Knows(John,x)  Knows(y,z) Q P   θ
Unification Example {x/Jane} {x/Bill,y/John} {y/John,x/Mother(John)} {x/Elizabeth, z 17 /John} For every unifiable pair of  expressions, there is a  Most General Unifier  MGU In the last case, we have two answers: θ = {y/John,x/z}, or θ = {y/John,x/John, z/John} {y/John,x/z} Unify (p,q)  =  θ   where Subst( θ ,p) = Subset( θ ,q) Suppose we have a query Knows(John,x), we need to unify Knows(John,x) with all sentences in KD. Knows(John,x)  Knows(John,Jane)  Knows(John,x) Knows(y,Bill)  Knows(John,x)  Knows(y,Mother(y)) Knows(John,x) Knows(z 17 ,Elizabeth)  Knows(John,x)  Knows(y,z) Q P   θ
Other Examples ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Generalized Modus Ponens (GMP) where  P i   θ  =  Q i   θ  for all  i A first-order inference rule, to find substitutions easily. ,[object Object],[object Object],P 1 ,  P 2 , … ,  P n ,  (  Q 1      Q 2     …     Q n    R ) Subst (R,  θ ) King ( John ) , Greedy ( y ) ,  ( King ( x ) , Greedy ( x )     Evil ( x )) Subst(Evil(x),  {x/John, y/John})
Generalized Modus Ponens (GMP) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Forward Chaining ,[object Object],[object Object],[object Object]
Example Knowledge Base ,[object Object],[object Object]
Example Knowledge Base contd. ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Forward Chaining Proof American ( x )    Weapon ( y )  Sells ( x,y,z ) Missile ( M 1 ) Owns ( Nono,M 1 )   Missile ( x )   Owns ( Nono,x ) Sells ( West,x,Nono )   Weapon(x) Missile ( x )    Enemy ( x,America )   Hostile ( x )  ,[object Object],[object Object], Criminal(x)  Hostile(x)
Forward Chaining Proof American ( x )    Weapon ( y )  Sells ( x,y,z ) Missile ( M 1 ) Owns ( Nono,M 1 )   Missile ( x )   Owns ( Nono,x ) Sells ( West,x,Nono )   Weapon(x) Missile ( x )    Enemy ( x,America )   Hostile ( x )  ,[object Object],[object Object], Criminal(x)  Hostile(x)
Forward Chaining Proof American ( x )    Weapon ( y )  Sells ( x,y,z ) Missile ( M 1 ) Owns ( Nono,M 1 )   Missile ( x )   Owns ( Nono,x ) Sells ( West,x,Nono )   Weapon(x) Missile ( x )    Enemy ( x,America )   Hostile ( x )  ,[object Object],[object Object], Criminal(x)  Hostile(x)
Forward Chaining Proof American ( x )    Weapon ( y )  Sells ( x,y,z ) Missile ( M 1 ) Owns ( Nono,M 1 )   Missile ( x )   Owns ( Nono,x ) Sells ( West,x,Nono )   Weapon(x) Missile ( x )    Enemy ( x,America )   Hostile ( x )  ,[object Object],[object Object], Criminal(x)  Hostile(x)
Forward Chaining Proof American ( x )    Weapon ( y )  Sells ( x,y,z ) Missile ( M 1 ) Owns ( Nono,M 1 )   Missile ( x )   Owns ( Nono,x ) Sells ( West,x,Nono )   Weapon(x) Missile ( x )    Enemy ( x,America )   Hostile ( x )  ,[object Object],[object Object], Criminal(x)  Hostile(x)
Forward Chaining Proof American ( x )    Weapon ( y )  Sells ( x,y,z ) Missile ( M 1 ) Owns ( Nono,M 1 )   Missile ( x )   Owns ( Nono,x ) Sells ( West,x,Nono )   Weapon(x) Missile ( x )    Enemy ( x,America )   Hostile ( x )  ,[object Object],[object Object], Criminal(x)  Hostile(x)
Properties of Forward Chaining ,[object Object],[object Object],[object Object],[object Object],[object Object]
Efficiency of Forward Chaining ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Backward Chaining ,[object Object],[object Object]
Backward chaining example American ( x )    Weapon ( y )  Sells ( x,y,z )  Criminal ( x ) Missile ( M 1 ) Owns ( Nono,M 1 )   Missile ( x )   Owns ( Nono,x ) Sells ( West,x,Nono )   Weapon(x) Missile ( x )    Enemy ( x,America )   Hostile ( x )  ,[object Object],[object Object], Hostile ( x )
Backward chaining example American ( x )    Weapon ( y )  Sells ( x,y,z ) Missile ( M 1 ) Owns ( Nono,M 1 )   Missile ( x )   Owns ( Nono,x ) Sells ( West,x,Nono )   Weapon(x) Missile ( x )    Enemy ( x,America )   Hostile ( x )  ,[object Object],[object Object], Criminal ( x )  Hostile(x)
Backward chaining example American ( x )    Weapon ( y )  Sells ( x,y,z ) Missile ( M 1 ) Owns ( Nono,M 1 )   Missile ( x )   Owns ( Nono,x ) Sells ( West,x,Nono )   Weapon(x) Missile ( x )    Enemy ( x,America )   Hostile ( x )  ,[object Object],[object Object], Criminal ( x )  Hostile(x)
Backward chaining example American ( x )    Weapon ( y )  Sells ( x,y,z ) Missile ( M 1 ) Owns ( Nono,M 1 )   Missile ( x )   Owns ( Nono,x ) Sells ( West,x,Nono )   Weapon(x) Missile ( x )    Enemy ( x,America )   Hostile ( x )  ,[object Object],[object Object], Criminal ( x )  Hostile(x)
Backward chaining example American ( x )    Weapon ( y )  Sells ( x,y,z ) Missile ( M 1 ) Owns ( Nono,M 1 )   Missile ( x )   Owns ( Nono,x ) Sells ( West,x,Nono )   Weapon(x) Missile ( x )    Enemy ( x,America )   Hostile ( x )  ,[object Object],[object Object], Criminal ( x )  Hostile(x)
Backward chaining example American ( x )    Weapon ( y )  Sells ( x,y,z ) Missile ( M 1 ) Owns ( Nono,M 1 )   Missile ( x )   Owns ( Nono,x ) Sells ( West,x,Nono )   Weapon(x) Missile ( x )    Enemy ( x,America )   Hostile ( x )  ,[object Object],[object Object], Criminal ( x )  Hostile(x)
Backward chaining example American ( x )    Weapon ( y )  Sells ( x,y,z ) Missile ( M 1 ) Owns ( Nono,M 1 )   Missile ( x )   Owns ( Nono,x ) Sells ( West,x,Nono )   Weapon(x) Missile ( x )    Enemy ( x,America )   Hostile ( x )  ,[object Object],[object Object], Criminal ( x )  Hostile(x)
Properties of Backward Chaining ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Forward  vs.  Backward Chaining ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Logic Programming ,[object Object],[object Object],[object Object],If B 1  and … and B n  then H ,[object Object],where implication would be interpreted as a solution of problem H given solutions of B 1  … B n .  ,[object Object],[object Object]
Logic Programming: Prolog ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Logic Programming: Prolog ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],?- sibling(Sara, Dina).  Yes  ?- father(Father, Child).  // enumerates all valid answers
Resolution in FOL
Resolution in FOL ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Conversion to CNF ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Conversion to CNF contd. ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Resolution in FOL ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Resolution in FOL (Example) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Resolution in FOL (Example)
Resolution in FOL (Example) A1 ,[object Object],A2 ,[object Object],B  Loves(y,x)      Animal(z)      Kills(x,z) C  Animal(x) Cat(Foxi) Loves(Ali,x) D Kills(Ali,Foxi)    Kills(Kais, Foxi) E Cat(Foxi) F  Cat(x)    Animal (x) G ,[object Object],H Animal (Foxi) E,F {x/Foxi} I ,[object Object],D,G {} J ,[object Object],A2,C {x/Ali, F(x)/x} K ,[object Object],J,A1 {F(x)/F(Ali), X/Ali} L ,[object Object],H,B {z/Foxi} M ,[object Object],I,L {x/Ali} N . M,K {y/G(Ali)}
Resolution in FOL (Another Example) ,[object Object],[object Object],[object Object],   American(x)      Weapon(y)      Sells(x,y,z)      Hostile(z)    Criminal(x)  Missile(x)      Owns(Nono,x)    Sells(West,x,Nano)  Enemy(x,America)    Hostile(x)   Missile(x)    Weapon(x) Owns(Nono,M 1 ) Missile(M 1 )  American(West)  Enemy(Nano,America)  Criminal (West)
Resolution in FOL (Another Example) 1    American(x)      Weapon(y)      Sells(x,y,z)      Hostile(z)    Criminal(x) 2  Missile(x)      Owns(Nono,x)    Sells(West,x,Nano) 3  Enemy(x,America)    Hostile(x)  4  Missile(x)    Weapon(x) 5 Owns(Nono,M 1 ) 6 Missile(M 1 )  7 American(West)  8 Enemy(Nano,America) 9  Criminal (West)
Resolution in FOL (Another Example) 1    American(x)      Weapon(y)      Sells(x,y,z)      Hostile(z)    Criminal(x) 2  Missile(x)      Owns(Nono,x)    Sells(West,x,Nano) 3  Enemy(x,America)    Hostile(x)  4  Missile(x)    Weapon(x) 5 Owns(Nono,M 1 ) 6 Missile(M 1 )  7 American(West)  8 Enemy(Nano,America) 9  Criminal (West) 10    American(West)      Weapon(y)      Sells(West,y,z)      Hostile(z)  1,9 {x/West} 11  Weapon(y)      Sells(West,y,z)      Hostile(z)  7,10 {x/West} 12  Missile(y)      Sells(West,y,z)      Hostile(z)  4,11 {x/y} 13  Sells(West,M 1 ,z)      Hostile(z) 6,12 {y/M 1 } 14  Missile(M 1 )      Owns(Nono, M 1 )      Hostile(Nano) 2,13 {x/M 1 , z/Nano} 15  Owns(Nono, M 1 )      Hostile(Nano) 6,14 {} 16  Hostile(Nano) 5,15 {} 17  Enemy(Nano,America) 3,16 {x/Nano} 18 . 8,17 {}
Resolution in FOL (Another Example) 9 10 11 12 13 14 15 16 17 18 1 7 6 2 6 4 5 3 8 Another representation (as Tree)
Summary ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Contenu connexe

Tendances

On fuzzy compact open topology
On fuzzy compact open topologyOn fuzzy compact open topology
On fuzzy compact open topology
bkrawat08
 
Data Complexity in EL Family of Description Logics
Data Complexity in EL Family of Description LogicsData Complexity in EL Family of Description Logics
Data Complexity in EL Family of Description Logics
Adila Krisnadhi
 
Local Closed World Semantics - DL 2011 Poster
Local Closed World Semantics - DL 2011 PosterLocal Closed World Semantics - DL 2011 Poster
Local Closed World Semantics - DL 2011 Poster
Adila Krisnadhi
 

Tendances (19)

Logic 2
Logic 2Logic 2
Logic 2
 
Per3 logika&pembuktian
Per3 logika&pembuktianPer3 logika&pembuktian
Per3 logika&pembuktian
 
First order logic
First order logicFirst order logic
First order logic
 
3 fol examples v2
3 fol examples v23 fol examples v2
3 fol examples v2
 
Predicate Logic
Predicate LogicPredicate Logic
Predicate Logic
 
W.cholamjiak
W.cholamjiakW.cholamjiak
W.cholamjiak
 
On fuzzy compact open topology
On fuzzy compact open topologyOn fuzzy compact open topology
On fuzzy compact open topology
 
Update 2
Update 2Update 2
Update 2
 
Fibrewise near compact and locally near compact spaces
Fibrewise near compact and locally near compact spacesFibrewise near compact and locally near compact spaces
Fibrewise near compact and locally near compact spaces
 
Data Complexity in EL Family of Description Logics
Data Complexity in EL Family of Description LogicsData Complexity in EL Family of Description Logics
Data Complexity in EL Family of Description Logics
 
Raices primitivas
Raices primitivasRaices primitivas
Raices primitivas
 
Discrete assignment
Discrete assignmentDiscrete assignment
Discrete assignment
 
True but Unprovable
True but UnprovableTrue but Unprovable
True but Unprovable
 
Lesson 5: Continuity
Lesson 5: ContinuityLesson 5: Continuity
Lesson 5: Continuity
 
First order logic
First order logicFirst order logic
First order logic
 
Overview prolog
Overview prologOverview prolog
Overview prolog
 
Local Closed World Semantics - DL 2011 Poster
Local Closed World Semantics - DL 2011 PosterLocal Closed World Semantics - DL 2011 Poster
Local Closed World Semantics - DL 2011 Poster
 
C2.0 propositional logic
C2.0 propositional logicC2.0 propositional logic
C2.0 propositional logic
 
3.6 Families ordered by inclusion
3.6 Families ordered by inclusion3.6 Families ordered by inclusion
3.6 Families ordered by inclusion
 

En vedette

Jarrar.lecture notes.aai.2011s.ch7.p logic
Jarrar.lecture notes.aai.2011s.ch7.p logicJarrar.lecture notes.aai.2011s.ch7.p logic
Jarrar.lecture notes.aai.2011s.ch7.p logic
PalGov
 
Jarrar.lecture notes.aai.2011s.ontology part1_introduction
Jarrar.lecture notes.aai.2011s.ontology part1_introductionJarrar.lecture notes.aai.2011s.ontology part1_introduction
Jarrar.lecture notes.aai.2011s.ontology part1_introduction
PalGov
 
Jarrar.lecture notes.aai.2011s.ontology part4_methodologies
Jarrar.lecture notes.aai.2011s.ontology part4_methodologiesJarrar.lecture notes.aai.2011s.ontology part4_methodologies
Jarrar.lecture notes.aai.2011s.ontology part4_methodologies
PalGov
 
Jarrar.lecture notes.aai.2011s.ontology part2_whatisontology
Jarrar.lecture notes.aai.2011s.ontology part2_whatisontologyJarrar.lecture notes.aai.2011s.ontology part2_whatisontology
Jarrar.lecture notes.aai.2011s.ontology part2_whatisontology
PalGov
 
Jarrar.lecture notes.aai.2011s.ch6.games
Jarrar.lecture notes.aai.2011s.ch6.gamesJarrar.lecture notes.aai.2011s.ch6.games
Jarrar.lecture notes.aai.2011s.ch6.games
PalGov
 
Jarrar.lecture notes.aai.2011s.ch2.intelligentagents
Jarrar.lecture notes.aai.2011s.ch2.intelligentagentsJarrar.lecture notes.aai.2011s.ch2.intelligentagents
Jarrar.lecture notes.aai.2011s.ch2.intelligentagents
PalGov
 
Jarrar.lecture notes.aai.2011s.ch4.informedsearch
Jarrar.lecture notes.aai.2011s.ch4.informedsearchJarrar.lecture notes.aai.2011s.ch4.informedsearch
Jarrar.lecture notes.aai.2011s.ch4.informedsearch
PalGov
 

En vedette (7)

Jarrar.lecture notes.aai.2011s.ch7.p logic
Jarrar.lecture notes.aai.2011s.ch7.p logicJarrar.lecture notes.aai.2011s.ch7.p logic
Jarrar.lecture notes.aai.2011s.ch7.p logic
 
Jarrar.lecture notes.aai.2011s.ontology part1_introduction
Jarrar.lecture notes.aai.2011s.ontology part1_introductionJarrar.lecture notes.aai.2011s.ontology part1_introduction
Jarrar.lecture notes.aai.2011s.ontology part1_introduction
 
Jarrar.lecture notes.aai.2011s.ontology part4_methodologies
Jarrar.lecture notes.aai.2011s.ontology part4_methodologiesJarrar.lecture notes.aai.2011s.ontology part4_methodologies
Jarrar.lecture notes.aai.2011s.ontology part4_methodologies
 
Jarrar.lecture notes.aai.2011s.ontology part2_whatisontology
Jarrar.lecture notes.aai.2011s.ontology part2_whatisontologyJarrar.lecture notes.aai.2011s.ontology part2_whatisontology
Jarrar.lecture notes.aai.2011s.ontology part2_whatisontology
 
Jarrar.lecture notes.aai.2011s.ch6.games
Jarrar.lecture notes.aai.2011s.ch6.gamesJarrar.lecture notes.aai.2011s.ch6.games
Jarrar.lecture notes.aai.2011s.ch6.games
 
Jarrar.lecture notes.aai.2011s.ch2.intelligentagents
Jarrar.lecture notes.aai.2011s.ch2.intelligentagentsJarrar.lecture notes.aai.2011s.ch2.intelligentagents
Jarrar.lecture notes.aai.2011s.ch2.intelligentagents
 
Jarrar.lecture notes.aai.2011s.ch4.informedsearch
Jarrar.lecture notes.aai.2011s.ch4.informedsearchJarrar.lecture notes.aai.2011s.ch4.informedsearch
Jarrar.lecture notes.aai.2011s.ch4.informedsearch
 

Similaire à Jarrar.lecture notes.aai.2011s.ch9.fol.inference

Exchanging More than Complete Data
Exchanging More than Complete DataExchanging More than Complete Data
Exchanging More than Complete Data
net2-project
 
Nested Quantifiers.pptx
Nested Quantifiers.pptxNested Quantifiers.pptx
Nested Quantifiers.pptx
Jeevan225779
 
introduction to Genifer -- Deduction
introduction to Genifer -- Deductionintroduction to Genifer -- Deduction
introduction to Genifer -- Deduction
Yan Yin
 
Jarrar.lecture notes.aai.2011s.ch8.fol.introduction
Jarrar.lecture notes.aai.2011s.ch8.fol.introductionJarrar.lecture notes.aai.2011s.ch8.fol.introduction
Jarrar.lecture notes.aai.2011s.ch8.fol.introduction
PalGov
 

Similaire à Jarrar.lecture notes.aai.2011s.ch9.fol.inference (20)

L03 ai - knowledge representation using logic
L03 ai - knowledge representation using logicL03 ai - knowledge representation using logic
L03 ai - knowledge representation using logic
 
Predicates
PredicatesPredicates
Predicates
 
9.class-notesr9.ppt
9.class-notesr9.ppt9.class-notesr9.ppt
9.class-notesr9.ppt
 
Exchanging More than Complete Data
Exchanging More than Complete DataExchanging More than Complete Data
Exchanging More than Complete Data
 
Fol
FolFol
Fol
 
lect14-semantics.ppt
lect14-semantics.pptlect14-semantics.ppt
lect14-semantics.ppt
 
Nested Quantifiers.pptx
Nested Quantifiers.pptxNested Quantifiers.pptx
Nested Quantifiers.pptx
 
Chapter 01 - p2.pdf
Chapter 01 - p2.pdfChapter 01 - p2.pdf
Chapter 01 - p2.pdf
 
introduction to Genifer -- Deduction
introduction to Genifer -- Deductionintroduction to Genifer -- Deduction
introduction to Genifer -- Deduction
 
lecture-inference-in-first-order-logic.pdf
lecture-inference-in-first-order-logic.pdflecture-inference-in-first-order-logic.pdf
lecture-inference-in-first-order-logic.pdf
 
01bkb04p.ppt
01bkb04p.ppt01bkb04p.ppt
01bkb04p.ppt
 
Math Assignment Help
Math Assignment HelpMath Assignment Help
Math Assignment Help
 
1606751772-ds-lecture-6.ppt
1606751772-ds-lecture-6.ppt1606751772-ds-lecture-6.ppt
1606751772-ds-lecture-6.ppt
 
Discreate structure presentation introduction
Discreate structure presentation introductionDiscreate structure presentation introduction
Discreate structure presentation introduction
 
Per3 logika
Per3 logikaPer3 logika
Per3 logika
 
FOLBUKCFAIZ.pptx
FOLBUKCFAIZ.pptxFOLBUKCFAIZ.pptx
FOLBUKCFAIZ.pptx
 
Cs229 notes8
Cs229 notes8Cs229 notes8
Cs229 notes8
 
Jarrar.lecture notes.aai.2011s.ch8.fol.introduction
Jarrar.lecture notes.aai.2011s.ch8.fol.introductionJarrar.lecture notes.aai.2011s.ch8.fol.introduction
Jarrar.lecture notes.aai.2011s.ch8.fol.introduction
 
lacl (1)
lacl (1)lacl (1)
lacl (1)
 
Semantics
SemanticsSemantics
Semantics
 

Plus de PalGov

Jarrar.lecture notes.aai.2011s.ch9.fol.inference
Jarrar.lecture notes.aai.2011s.ch9.fol.inferenceJarrar.lecture notes.aai.2011s.ch9.fol.inference
Jarrar.lecture notes.aai.2011s.ch9.fol.inference
PalGov
 
Jarrar.lecture notes.aai.2011s.ontology part5_egovernmentcasestudy
Jarrar.lecture notes.aai.2011s.ontology part5_egovernmentcasestudyJarrar.lecture notes.aai.2011s.ontology part5_egovernmentcasestudy
Jarrar.lecture notes.aai.2011s.ontology part5_egovernmentcasestudy
PalGov
 
Jarrar.lecture notes.aai.2011s.ontology part3_double-articulation
Jarrar.lecture notes.aai.2011s.ontology part3_double-articulationJarrar.lecture notes.aai.2011s.ontology part3_double-articulation
Jarrar.lecture notes.aai.2011s.ontology part3_double-articulation
PalGov
 
Jarrar.lecture notes.aai.2011s.descriptionlogic
Jarrar.lecture notes.aai.2011s.descriptionlogicJarrar.lecture notes.aai.2011s.descriptionlogic
Jarrar.lecture notes.aai.2011s.descriptionlogic
PalGov
 
Jarrar.lecture notes.aai.2011s.ch3.uniformedsearch
Jarrar.lecture notes.aai.2011s.ch3.uniformedsearchJarrar.lecture notes.aai.2011s.ch3.uniformedsearch
Jarrar.lecture notes.aai.2011s.ch3.uniformedsearch
PalGov
 
Jarrar.lecture notes.aai.2011s.ch2.intelligentagents
Jarrar.lecture notes.aai.2011s.ch2.intelligentagentsJarrar.lecture notes.aai.2011s.ch2.intelligentagents
Jarrar.lecture notes.aai.2011s.ch2.intelligentagents
PalGov
 

Plus de PalGov (6)

Jarrar.lecture notes.aai.2011s.ch9.fol.inference
Jarrar.lecture notes.aai.2011s.ch9.fol.inferenceJarrar.lecture notes.aai.2011s.ch9.fol.inference
Jarrar.lecture notes.aai.2011s.ch9.fol.inference
 
Jarrar.lecture notes.aai.2011s.ontology part5_egovernmentcasestudy
Jarrar.lecture notes.aai.2011s.ontology part5_egovernmentcasestudyJarrar.lecture notes.aai.2011s.ontology part5_egovernmentcasestudy
Jarrar.lecture notes.aai.2011s.ontology part5_egovernmentcasestudy
 
Jarrar.lecture notes.aai.2011s.ontology part3_double-articulation
Jarrar.lecture notes.aai.2011s.ontology part3_double-articulationJarrar.lecture notes.aai.2011s.ontology part3_double-articulation
Jarrar.lecture notes.aai.2011s.ontology part3_double-articulation
 
Jarrar.lecture notes.aai.2011s.descriptionlogic
Jarrar.lecture notes.aai.2011s.descriptionlogicJarrar.lecture notes.aai.2011s.descriptionlogic
Jarrar.lecture notes.aai.2011s.descriptionlogic
 
Jarrar.lecture notes.aai.2011s.ch3.uniformedsearch
Jarrar.lecture notes.aai.2011s.ch3.uniformedsearchJarrar.lecture notes.aai.2011s.ch3.uniformedsearch
Jarrar.lecture notes.aai.2011s.ch3.uniformedsearch
 
Jarrar.lecture notes.aai.2011s.ch2.intelligentagents
Jarrar.lecture notes.aai.2011s.ch2.intelligentagentsJarrar.lecture notes.aai.2011s.ch2.intelligentagents
Jarrar.lecture notes.aai.2011s.ch2.intelligentagents
 

Dernier

Salient Features of India constitution especially power and functions
Salient Features of India constitution especially power and functionsSalient Features of India constitution especially power and functions
Salient Features of India constitution especially power and functions
KarakKing
 
The basics of sentences session 3pptx.pptx
The basics of sentences session 3pptx.pptxThe basics of sentences session 3pptx.pptx
The basics of sentences session 3pptx.pptx
heathfieldcps1
 

Dernier (20)

How to Create and Manage Wizard in Odoo 17
How to Create and Manage Wizard in Odoo 17How to Create and Manage Wizard in Odoo 17
How to Create and Manage Wizard in Odoo 17
 
Understanding Accommodations and Modifications
Understanding  Accommodations and ModificationsUnderstanding  Accommodations and Modifications
Understanding Accommodations and Modifications
 
SOC 101 Demonstration of Learning Presentation
SOC 101 Demonstration of Learning PresentationSOC 101 Demonstration of Learning Presentation
SOC 101 Demonstration of Learning Presentation
 
Salient Features of India constitution especially power and functions
Salient Features of India constitution especially power and functionsSalient Features of India constitution especially power and functions
Salient Features of India constitution especially power and functions
 
Exploring_the_Narrative_Style_of_Amitav_Ghoshs_Gun_Island.pptx
Exploring_the_Narrative_Style_of_Amitav_Ghoshs_Gun_Island.pptxExploring_the_Narrative_Style_of_Amitav_Ghoshs_Gun_Island.pptx
Exploring_the_Narrative_Style_of_Amitav_Ghoshs_Gun_Island.pptx
 
REMIFENTANIL: An Ultra short acting opioid.pptx
REMIFENTANIL: An Ultra short acting opioid.pptxREMIFENTANIL: An Ultra short acting opioid.pptx
REMIFENTANIL: An Ultra short acting opioid.pptx
 
Single or Multiple melodic lines structure
Single or Multiple melodic lines structureSingle or Multiple melodic lines structure
Single or Multiple melodic lines structure
 
Google Gemini An AI Revolution in Education.pptx
Google Gemini An AI Revolution in Education.pptxGoogle Gemini An AI Revolution in Education.pptx
Google Gemini An AI Revolution in Education.pptx
 
Accessible Digital Futures project (20/03/2024)
Accessible Digital Futures project (20/03/2024)Accessible Digital Futures project (20/03/2024)
Accessible Digital Futures project (20/03/2024)
 
Unit 3 Emotional Intelligence and Spiritual Intelligence.pdf
Unit 3 Emotional Intelligence and Spiritual Intelligence.pdfUnit 3 Emotional Intelligence and Spiritual Intelligence.pdf
Unit 3 Emotional Intelligence and Spiritual Intelligence.pdf
 
The basics of sentences session 3pptx.pptx
The basics of sentences session 3pptx.pptxThe basics of sentences session 3pptx.pptx
The basics of sentences session 3pptx.pptx
 
Fostering Friendships - Enhancing Social Bonds in the Classroom
Fostering Friendships - Enhancing Social Bonds  in the ClassroomFostering Friendships - Enhancing Social Bonds  in the Classroom
Fostering Friendships - Enhancing Social Bonds in the Classroom
 
COMMUNICATING NEGATIVE NEWS - APPROACHES .pptx
COMMUNICATING NEGATIVE NEWS - APPROACHES .pptxCOMMUNICATING NEGATIVE NEWS - APPROACHES .pptx
COMMUNICATING NEGATIVE NEWS - APPROACHES .pptx
 
Wellbeing inclusion and digital dystopias.pptx
Wellbeing inclusion and digital dystopias.pptxWellbeing inclusion and digital dystopias.pptx
Wellbeing inclusion and digital dystopias.pptx
 
Sociology 101 Demonstration of Learning Exhibit
Sociology 101 Demonstration of Learning ExhibitSociology 101 Demonstration of Learning Exhibit
Sociology 101 Demonstration of Learning Exhibit
 
Jamworks pilot and AI at Jisc (20/03/2024)
Jamworks pilot and AI at Jisc (20/03/2024)Jamworks pilot and AI at Jisc (20/03/2024)
Jamworks pilot and AI at Jisc (20/03/2024)
 
ICT role in 21st century education and it's challenges.
ICT role in 21st century education and it's challenges.ICT role in 21st century education and it's challenges.
ICT role in 21st century education and it's challenges.
 
On_Translating_a_Tamil_Poem_by_A_K_Ramanujan.pptx
On_Translating_a_Tamil_Poem_by_A_K_Ramanujan.pptxOn_Translating_a_Tamil_Poem_by_A_K_Ramanujan.pptx
On_Translating_a_Tamil_Poem_by_A_K_Ramanujan.pptx
 
Graduate Outcomes Presentation Slides - English
Graduate Outcomes Presentation Slides - EnglishGraduate Outcomes Presentation Slides - English
Graduate Outcomes Presentation Slides - English
 
How to Manage Global Discount in Odoo 17 POS
How to Manage Global Discount in Odoo 17 POSHow to Manage Global Discount in Odoo 17 POS
How to Manage Global Discount in Odoo 17 POS
 

Jarrar.lecture notes.aai.2011s.ch9.fol.inference

  • 1. Dr. Mustafa Jarrar [email_address] www.jarrar.info University of Birzeit Chapter 9 Inference in first-order logic Advanced Artificial Intelligence (SCOM7341) Lecture Notes University of Birzeit 2 nd Semester, 2011
  • 2. Motivation: Knowledge Bases vs. Databases The DB is a set of tuples, and queries are perfromed truth rvaluation. ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Wffs : Proof xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx Provability Query Answer Knowledge Base System Query Proof Theoretic View Constraints DBMS Transactions i.e insert, update, delete... Query Query Answer Model Theoretic View The KB is a set of formulae and the query evaluation is to prove that the result is provable.
  • 3.
  • 4.
  • 5.
  • 6.
  • 7.
  • 8.
  • 9.
  • 10.
  • 11.
  • 12.
  • 13. Unification Example Unify (p,q) = θ where Subst( θ ,p) = Subset( θ ,q) Suppose we have a query Knows(John,x), we need to unify Knows(John,x) with all sentences in KD. Knows(John,x) Knows(John,Jane) Knows(John,x) Knows(y,Bill) Knows(John,x) Knows(y,Mother(y)) Knows(John,x) Knows(x,Elizabeth) Q P θ
  • 14. Unification Example {x/Jane} Unify (p,q) = θ where Subst( θ ,p) = Subset( θ ,q) Suppose we have a query Knows(John,x), we need to unify Knows(John,x) with all sentences in KD. Knows(John,x) Knows(John,Jane) Knows(John,x) Knows(y,Bill) Knows(John,x) Knows(y,Mother(y)) Knows(John,x) Knows(x,Elizabeth) Q P θ
  • 15. Unification Example {x/Jane} {x/Bill,y/John} Unify (p,q) = θ where Subst( θ ,p) = Subset( θ ,q) Suppose we have a query Knows(John,x), we need to unify Knows(John,x) with all sentences in KD. Knows(John,x) Knows(John,Jane) Knows(John,x) Knows(y,Bill) Knows(John,x) Knows(y,Mother(y)) Knows(John,x) Knows(x,Elizabeth) Q P θ
  • 16. Unification Example {x/Jane} {x/Bill,y/John} {y/John,x/Mother(John)} Unify (p,q) = θ where Subst( θ ,p) = Subset( θ ,q) Suppose we have a query Knows(John,x), we need to unify Knows(John,x) with all sentences in KD. Knows(John,x) Knows(John,Jane) Knows(John,x) Knows(y,Bill) Knows(John,x) Knows(y,Mother(y)) Knows(John,x) Knows(x,Elizabeth) Q P θ
  • 17.
  • 18.
  • 19. Unification Example {x/Jane} {x/Bill,y/John} {y/John,x/Mother(John)} {x/Elizabeth, z 17 /John} Unify (p,q) = θ where Subst( θ ,p) = Subset( θ ,q) Suppose we have a query Knows(John,x), we need to unify Knows(John,x) with all sentences in KD. Knows(John,x) Knows(John,Jane) Knows(John,x) Knows(y,Bill) Knows(John,x) Knows(y,Mother(y)) Knows(John,x) Knows(z 17 ,Elizabeth) Q P θ
  • 20. Unification Example {x/Jane} {x/Bill,y/John} {y/John,x/Mother(John)} {x/Elizabeth, z 17 /John} In the last case, we have two answers: θ = {y/John,x/z}, or θ = {y/John,x/John, z/John} ?? This first unification is more general, as it places fewer restrictions on the values of the variables. Unify (p,q) = θ where Subst( θ ,p) = Subset( θ ,q) Suppose we have a query Knows(John,x), we need to unify Knows(John,x) with all sentences in KD. Knows(John,x) Knows(John,Jane) Knows(John,x) Knows(y,Bill) Knows(John,x) Knows(y,Mother(y)) Knows(John,x) Knows(z 17 ,Elizabeth) Knows(John,x) Knows(y,z) Q P θ
  • 21. Unification Example {x/Jane} {x/Bill,y/John} {y/John,x/Mother(John)} {x/Elizabeth, z 17 /John} For every unifiable pair of expressions, there is a Most General Unifier MGU In the last case, we have two answers: θ = {y/John,x/z}, or θ = {y/John,x/John, z/John} {y/John,x/z} Unify (p,q) = θ where Subst( θ ,p) = Subset( θ ,q) Suppose we have a query Knows(John,x), we need to unify Knows(John,x) with all sentences in KD. Knows(John,x) Knows(John,Jane) Knows(John,x) Knows(y,Bill) Knows(John,x) Knows(y,Mother(y)) Knows(John,x) Knows(z 17 ,Elizabeth) Knows(John,x) Knows(y,z) Q P θ
  • 22.
  • 23.
  • 24.
  • 25.
  • 26.
  • 27.
  • 28.
  • 29.
  • 30.
  • 31.
  • 32.
  • 33.
  • 34.
  • 35.
  • 36.
  • 37.
  • 38.
  • 39.
  • 40.
  • 41.
  • 42.
  • 43.
  • 44.
  • 45.
  • 46.
  • 47.
  • 48.
  • 50.
  • 51.
  • 52.
  • 53.
  • 54.
  • 55.
  • 56.
  • 57.
  • 58. Resolution in FOL (Another Example) 1  American(x)   Weapon(y)   Sells(x,y,z)   Hostile(z)  Criminal(x) 2  Missile(x)   Owns(Nono,x)  Sells(West,x,Nano) 3  Enemy(x,America)  Hostile(x) 4  Missile(x)  Weapon(x) 5 Owns(Nono,M 1 ) 6 Missile(M 1 ) 7 American(West) 8 Enemy(Nano,America) 9  Criminal (West)
  • 59. Resolution in FOL (Another Example) 1  American(x)   Weapon(y)   Sells(x,y,z)   Hostile(z)  Criminal(x) 2  Missile(x)   Owns(Nono,x)  Sells(West,x,Nano) 3  Enemy(x,America)  Hostile(x) 4  Missile(x)  Weapon(x) 5 Owns(Nono,M 1 ) 6 Missile(M 1 ) 7 American(West) 8 Enemy(Nano,America) 9  Criminal (West) 10  American(West)   Weapon(y)   Sells(West,y,z)   Hostile(z) 1,9 {x/West} 11  Weapon(y)   Sells(West,y,z)   Hostile(z) 7,10 {x/West} 12  Missile(y)   Sells(West,y,z)   Hostile(z) 4,11 {x/y} 13  Sells(West,M 1 ,z)   Hostile(z) 6,12 {y/M 1 } 14  Missile(M 1 )   Owns(Nono, M 1 )   Hostile(Nano) 2,13 {x/M 1 , z/Nano} 15  Owns(Nono, M 1 )   Hostile(Nano) 6,14 {} 16  Hostile(Nano) 5,15 {} 17  Enemy(Nano,America) 3,16 {x/Nano} 18 . 8,17 {}
  • 60. Resolution in FOL (Another Example) 9 10 11 12 13 14 15 16 17 18 1 7 6 2 6 4 5 3 8 Another representation (as Tree)
  • 61.