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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]

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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
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  • 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.