Using Resolution to Prove Theorems in Artificial Intelligence

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Using Resolution to Prove Theorems in Artificial Intelligence

Using Resolution to Prove Theorems 

  • The first step to use resolution as a rule of inference we have to convert wffs into clauses.    
  • If ∆ |== w then set of clauses are also logically entailed from ∆. 
  • Resolution refutation can be used to prove a wff w from ∆. 
  • The first step is to negate w, and this negation is converted to clause form and this is added to clause form of ∆. 
  • Until we deduce an empty clause the resolution is applied. 
  • Let us consider the courier delivery robot. Let the couriers delivered to room 20 are smaller than any of the ones in 27. It can be denoted as
    • (∀x, y) {[Courier(x) ∧ Courier(y) ∧ Inroom(x, 20) ∧ Inroom(27)] ⊃ Smaller(x, y) 
  • Converting the above wff into clause form and denoting Courier as C and Inroom as I and smaller as S we have, 
    • ᆨC(x) ∨ á†¨C(y) '.' ᆨI(x, 20) ∨ ᆨ(x, 27) ∨ S(x, y) 
  • If the robot knows the Courier B is in either of the rooms it can't know which is smaller. But it knows that A is in room 20 and A is not smaller than Courier B. 
      • C(B) 
      • C(A), 
      • I(B, 20)∨ I(B, 27). 
      • I(A, 20). 
      • ᆨS(A, B). 
    Now the robot which knows this information can prove that B is in room 20. By starting with the expression that B should not be in 20 the proof continues. It is shown in Figure. 
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Fig. A Resolution Refutation
    The information i.e. wffs known to the robot are present at the right side of the Figure and which has to be proved i.e. B is not in room 20 is shown at the left hand side. 

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