KR: Formalizing Sentences and Queries
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Questions and Answers

What is the formalization of the sentence 'Tom is John's child'?

child(tom, john)

What is the definition of a grandchild in terms of the child relation?

grandchild(X, Y) ⇔ (child(X, Z) ∧ child(Z, Y))

How would you formalize the sentence 'sam is an eagle'?

eagle(sam)

What is the conclusion of the query grandfather(a, X)?

<p>X = c</p> Signup and view all the answers

What is backtracking in the context of knowledge representation?

<p>The process of going back to a previous goal and trying to resatisfy it.</p> Signup and view all the answers

What are the different types of belief in fuzzy logic?

<p>Possible, Probable, Certain, Impossible, and Plausible</p> Signup and view all the answers

What is Uncertainty, and what are its consequences?

<p>Uncertainty is a lack of information to formulate a decision, and it may result in making poor or bad decisions.</p> Signup and view all the answers

How do expert systems deal with uncertainty?

<p>Expert systems provide an advantage when dealing with uncertainty as compared to decision trees, and they use probability theory to deal with theories of uncertainty.</p> Signup and view all the answers

What is the significance of probability theory in dealing with uncertainty?

<p>Probability theory is devoted to dealing with theories of uncertainty, and it has many theories, each with advantages and disadvantages.</p> Signup and view all the answers

What is the difference between expert systems and decision trees in dealing with uncertainty?

<p>Expert systems do not require all the facts to be known to arrive at an outcome, whereas decision trees require all the facts to be known to arrive at an outcome.</p> Signup and view all the answers

Study Notes

Knowledge Representation (KR)

  • Formalize sentences using predicates:
    • child(tom, john) represents "Tom is John's child"
    • child(ann, tom) represents "Ann is Tom's child"
    • child(john, mark) represents "John is Mark's child"
    • child(alice, john) represents "Alice is John's child"
    • grandchild(X, Y) <=> child(X, Z) ∧ child(Z, Y) represents "The grandchild of a person is a child of a child of this person"

Query Answers

  • Is Ann a child of Tom? -> Yes
  • Who is a grandchild of Ann? -> No information provided
  • Who is a grandchild of whom? -> No information provided

Example

  • Formalize the scenario:
    • bird(tom) represents "Tom is a bird"
    • bird(sam) represents "Sam is a bird"
    • bird(donald) represents "Donald is a bird"
    • eagle(sam) represents "Sam is an eagle"
    • penguin(tom) represents "Tom is a penguin"
    • duck(donald) represents "Donald is a duck"
    • fly(x) <=> feather(x) ∧ bird(x) represents "Birds fly if they have feathers"
    • penguin(x) => ¬feather(x) represents "Penguins do not have feathers"

Definite Program

  • grandfather(X, Z) <=> father(X, Y) ∧ parent(Y, Z) represents "A grandfather is a father of a parent"
  • parent(X, Y) ∨ father(X, Y) represents "A parent is either a father or a mother"
  • father(a, b) represents "A is the father of B"
  • mother(b, c) represents "B is the mother of C"
  • Query: grandfather(a, X) -> X = c

Backtracking

  • Backtracking is the process of going back to a previous goal and trying to resatisfy it, i.e., find another way of satisfying it.
  • Example:
    • Father(john, mary) represents "John is the father of Mary"
    • Father(john, mark) represents "John is the father of Mark"
    • Father(john, francis) represents "John is the father of Francis"
    • Father(gavin, lucy) represents "Gavin is the father of Lucy"
    • Rich(jane) represents "Jane is rich"
    • Rich(john) represents "John is rich"
    • Rich(gavin) represents "Gavin is rich"
    • Rich_father(X, Y) :- rich(X), father(X, Y) represents "A rich father is a father who is rich"
  • Query: Rich_father(A, B) -> Solution using backtrack method

Fuzzy Logic

  • Types of Belief:
    • Possible: no matter how remote, the hypothesis cannot be ruled out
    • Probable: there is some evidence favoring the hypothesis but not enough to prove it
    • Certain: evidence is logically true or false
    • Impossible: it is false
    • Plausible: more than a possibility exists
  • Uncertainty:
    • Lack of information to formulate a decision
    • May result in making poor or bad decisions
    • Requires reasoning under uncertainty along with common sense
  • Expert Systems:
    • Provide an advantage when dealing with uncertainty compared to decision trees
    • With decision trees, all facts must be known to arrive at an outcome
    • Probability theory is devoted to dealing with theories of uncertainty

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Practice formalizing sentences and answering queries related to family relationships, using knowledge representation techniques.

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