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Artificial Intelligence




                    Notes          5.  A ........................... is an artificial intelligence data structure used to divide knowledge into
                                       substructures by representing “stereotyped situations.”
                                   6.  The frame contains ........................... on how to use the frame, what to expect next, and what
                                       to do when these expectations are not met.
                                   7.  ...........................  rules,  which  are  arguably  the  most  common  form  of  knowledge
                                       representation in Artificial  Intelligence, are ambiguous.
                                   8.  A ........................... network is a network which represents semantic relations between the
                                       concepts. This is often used as a form of knowledge representation.

                                   5.3 Predicate Logic to Represent Knowledge


                                   Here we will emphasize main ethics enclosed in knowledge representation. Particularly predicate
                                   logic will be met in other knowledge representation systems and analysis ways.
                                   The following standard logic symbols are used generally:

                                   For  all             
                                   There exists         
                                   Implies             

                                   Not                  
                                   Or                   
                                   And                  
                                   Now we provide an example of how predicate logic is accessed to represent knowledge. There
                                   are other methods but this form is well-liked.


                                          Example: Consider the following:
                                      Sachin is a mega star.
                                      Mega stars are rich.

                                      Rich people have speedy cars.
                                      Fast cars take a lot of petrol.
                                   and strive to sketch the conclusion: Sachin’s car takes a lot of petrol.

                                   Thus we can convert Sachin is a mega star into: mega_star(sachin) and Mega stars are rich into:   m:
                                   mega_star(m)   rich(m)
                                   Rich people contain fast cars, the third axiom is more complicated:

                                      Is cars a relation and so car(c,m) says that case c is m’s car. OR
                                      Is cars a function? Thus we may have car_of(m).
                                   Consider that cars is a relation then axiom 3 may be written:   c,m: car(c,m) rich(m)  fast(c).
                                   The fourth axiom is a common statement regarding fast cars. Suppose consume(c) signify that car
                                   c takes a lot of petrol. So we may write:  c: [ fast(c) m:car(c,m)   consume(c) .

                                   Is this enough? no! — Does sachin have a car? We want the car_of function after all (and addition
                                   to car):   c:car(car_of(m),m). The effect of applying car_ofto m is m’s car. The concluding set of




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