1. Which search strategy is also called as blind search?
a) Uninformed search
b) Informed search
c) Simple reflex search
d) All of the mentioned
Answer: a
Explanation: In blind search, We can search the states without having any additional information. So uninformed search method is blind search.

2. How many types are available in uninformed search method?
a) 3
b) 4
c) 5
d) 6
Answer: c
Explanation: The five types of uninformed search method are Breadth-first, Uniform-cost, Depth-first, Depth-limited and Bidirectional search.

3. Which search is implemented with an empty first-in-first-out queue?
a) Depth-first search
b) Breadth-first search
c) Bidirectional search
d) None of the mentioned
Answer: b
Explanation: Because of FIFO queue, it will assure that the nodes that are visited first will be expanded first.

4. When is breadth-first search is optimal?
a) When there is less number of nodes
b) When all step costs are equal
c) When all step costs are unequal
d) None of the mentioned
Answer: b
Explanation: Because it always expands the shallowest unexpanded node.

5. How many successors are generated in backtracking search?
a) 1
b) 2
c) 3
d) 4
Answer: a
Explanation: Each partially expanded node remembers which successor to generate next because of these conditions, it uses less memory.

6. What is the space complexity of Depth-first search?
a) O(b)
b) O(bl)
c) O(m)
d) O(bm)
Answer: d
Explanation: O(bm) is the space complexity where b is the branching factor and m is the maximum depth of the search tree.

7. How many parts does a problem consists of?
a) 1
b) 2
c) 3
d) 4
Answer: d
Explanation: The four parts of the problem are initial state, set of actions, goal test and path cost.

8. Which algorithm is used to solve any kind of problem?
a) Breadth-first algorithm
b) Tree algorithm
c) Bidirectional search algorithm
d) None of the mentioned
Answer: b
Explanation: Tree algorithm is used because specific variants of the algorithm embed different strategies.

9. Which search algorithm imposes a fixed depth limit on nodes?
a) Depth-limited search
b) Depth-first search
c) Iterative deepening search
d) Bidirectional search
Answer: a
Explanation: None.

10. Which search implements stack operation for searching the states?
a) Depth-limited search
b) Depth-first search
c) Breadth-first search
d) None of the mentioned
Answer: b

11. What is the other name of informed search strategy?
a) Simple search
b) Heuristic search
c) Online search
d) None of the mentioned
Answer: b
Explanation: A key point of informed search strategy is heuristic function, So it is called as heuristic function.

12. How many types of informed search method are in artificial intelligence?
a) 1
b) 2
c) 3
d) 4
Answer: d
Explanation: The four types of informed search method are best-first search, Greedy best-first search, A* search and memory bounded heuristic search.

13. Which search uses the problem specific knowledge beyond the definition of the problem?
a) Informed search
b) Depth-first search
c) Breadth-first search
d) Uninformed search
Answer: a
Explanation: Informed search can solve the problem beyond the function definition, So does it can find the solution more efficiently.

14. Which function will select the lowest expansion node at first for evaluation?
a) Greedy best-first search
b) Best-first search
c) Depth-first search
d) None of the mentioned
Answer: b
Explanation: The lowest expansion node is selected because the evaluation measures distance to the goal.

15. What is the heuristic function of greedy best-first search?
a) f(n) != h(n)
b) f(n) < h(n)
c) f(n) = h(n)
d) f(n) > h(n)
Answer: c
Explanation: None.

16. Which search uses only the linear space for searching?
a) Best-first search
b) Recursive best-first search
c) Depth-first search
d) None of the mentioned
Answer: b
Explanation: Recursive best-first search will mimic the operation of standard best-first search, but using only the linear space.

17. Which method is used to search better by learning?
a) Best-first search
b) Depth-first search
c) Metalevel state space
d) None of the mentioned
Answer: c
Explanation: This search strategy will help to problem solving efficiency by using learning.

18. Which search is complete and optimal when h(n) is consistent?
a) Best-first search
b) Depth-first search
c) Both Best-first & Depth-first search
d) A* search
Answer: d
Explanation: None.

19. Which is used to improve the performance of heuristic search?
a) Quality of nodes
b) Quality of heuristic function
c) Simple form of nodes
d) None of the mentioned
Answer: b
Explanation: Good heuristic can be constructed by relaxing the problem, So the performance of heuristic search can be improved.

20. Which search method will expand the node that is closest to the goal?
a) Best-first search
b) Greedy best-first search
c) A* search
d) None of the mentioned
Answer: b
Explanation: Because of using greedy best-first search, It will quickly lead to the solution of the problem.

21. In many problems the path to goal is irrelevant, this class of problems can be solved using ____________
a) Informed Search Techniques
b) Uninformed Search Techniques
c) Local Search Techniques
d) Informed & Uninformed Search Techniques
Answer: c
Explanation: If the path to the goal does not matter, we might consider a different class of algorithms, ones that do not worry about paths at all. Local search algorithms operate using a single current state (rather than multiple paths) and generally move only to neighbors of that state.

22. Though local search algorithms are not systematic, key advantages would include __________
a) Less memory
b) More time
c) Finds a solution in large infinite space
d) Less memory & Finds a solution in large infinite space
Answer: d
Explanation: Two advantages: (1) they use very little memory-usually a constant amount; and (2) they can often find reasonable solutions in large or infinite (continuous) state spaces for which systematic algorithms are unsuitable.

23. A complete, local search algorithm always finds goal if one exists, an optimal algorithm always finds a global minimum/maximum.
a) True
b) False
Answer: a
Explanation: An algorithm is complete if it finds a solution if exists and optimal if finds optimal goal (minimum or maximum).

24. _______________ Is an algorithm, a loop that continually moves in the direction of increasing value – that is uphill.
a) Up-Hill Search
b) Hill-Climbing
c) Hill algorithm
d) Reverse-Down-Hill search
Answer: b
Explanation: Refer the definition of Hill-Climbing approach.

25. When will Hill-Climbing algorithm terminate?
a) Stopping criterion met
b) Global Min/Max is achieved
c) No neighbor has higher value
d) All of the mentioned
Answer: c
Explanation: When no neighbor is having higher value, algorithm terminates fetching local min/max.

26. What are the main cons of hill-climbing search?
a) Terminates at local optimum & Does not find optimum solution
b) Terminates at global optimum & Does not find optimum solution
c) Does not find optimum solution & Fail to find a solution
d) Fail to find a solution
View Answer
Answer: a
Explanation: Algorithm terminates at local optimum values, hence fails to find optimum solution.

27. Stochastic hill climbing chooses at random from among the uphill moves; the probability of selection can vary with the steepness of the uphil1 move.
a) True
b) False
Answer: a
Explanation: Refer to the definition of variants of hill-climbing search.

28. Hill climbing sometimes called ____________ because it grabs a good neighbor state without thinking ahead about where to go next.
a) Needy local search
b) Heuristic local search
c) Greedy local search
d) Optimal local search
Answer: c
Explanation: None.

29. Hill-Climbing approach stuck for which of the following reasons?
a) Local maxima
b) Ridges
c) Plateaux
d) All of the mentioned
Answer: d
Explanation: Local maxima: a local maximum is a peak that is higher than each of its neighboring states, but lower than the global maximum. Ridges: Ridges result in a sequence of local maxima that is very difficult for greedy algorithms to navigate. Plateaux: a plateau is an area of the state space landscape where the evaluation function is flat.

30. ___________ algorithm keeps track of k states rather than just one.
a) Hill-Climbing search
b) Local Beam search
c) Stochastic hill-climbing search
d) Random restart hill-climbing search
Answer: b
Explanation: Refer to the definition of Local Beam Search algorithm.

31. A genetic algorithm (or GA) is a variant of stochastic beam search in which successor states are generated by combining two parent states, rather than by modifying a single state.
a) True
b) False
Answer: a
Explanation: Stochastic beam search, analogous to stochastic hill climbing, helps to alleviate this problem. Instead of choosing the best k from the pool of candidate successors, stochastic beam search chooses k successors at random, with the probability of choosing a given successor being an increasing function of its value.

32. What are the two main features of Genetic Algorithm?
a) Fitness function & Crossover techniques
b) Crossover techniques & Random mutation
c) Individuals among the population & Random mutation
d) Random mutation & Fitness function
Answer: a
Explanation: Fitness function helps choosing individuals from the population and Crossover techniques defines the offspring generated.

33. Searching using query on Internet is, use of ___________ type of agent.
a) Offline agent
b) Online agent
c) Both Offline & Online agent
d) Goal Based & Online agent
Answer: d
Explanation: Refer to the definitions of both the type of agent.

1. There exist only two types of quantifiers, Universal Quantification and Existential Quantification.
a) True
b) False
Answer: a
Explanation: None.

2. Translate the following statement into FOL.
“For every a, if a is a philosopher, then a is a scholar”
a) ∀ a philosopher(a) scholar(a)
b) ∃ a philosopher(a) scholar(a)
c) All of the mentioned
d) None of the mentioned
Answer: a
Explanation: None.

3. A _________ is used to demonstrate, on a purely syntactic basis, that one formula is a logical consequence of another formula.
a) Deductive Systems
b) Inductive Systems
c) Reasoning with Knowledge Based Systems
d) Search Based Systems
Answer: a
Explanation: Refer the definition of Deductive based systems.

4. The statement comprising the limitations of FOL is/are ____________
a) Expressiveness
b) Formalizing Natural Languages
c) Many-sorted Logic
d) All of the mentioned
Answer: d
Explanation: The Löwenheim–Skolem theorem shows that if a first-order theory has any infinite model, then it has infinite models of every cardinality. In particular, no first-order theory with an infinite model can be categorical. Thus there is no first-order theory whose only model has the set of natural numbers as its domain, or whose only model has the set of real numbers as its domain. Many extensions of first-order logic, including infinitely logics and higher-order logics, are more expressive in the sense that they do permit categorical axiomatizations of the natural numbers or real numbers. This expressiveness comes at a meta-logical cost, however: by Lindström’s theorem, the compactness theorem and the downward Löwenheim–Skolem theorem cannot hold in any logic stronger than first-order.
Formalizing Natural Languages : First-order logic is able to formalize many simple quantifier constructions in natural language, such as “every person who lives in Perth lives in Australia”. But there are many more complicated features of natural language that cannot be expressed in (single-sorted) first-order logic.
Many-sorted Logic: Ordinary first-order interpretations have a single domain of discourse over which all quantifiers range. Many-sorted first-order logic allows variables to have different sorts, which have different domains.

5. A common convention is:
• is evaluated first
• and are evaluated next
• Quantifiers are evaluated next
• is evaluated last.
a) True
b) False
Answer: a
Explanation: None.

6. A Term is either an individual constant (a 0-ary function), or a variable, or an n-ary function applied to n terms: F(t1 t2 ..tn).
a) True
b) False
Answer: a
Explanation: Definition of term in FOL.

7. First Order Logic is also known as ___________
a) First Order Predicate Calculus
b) Quantification Theory
c) Lower Order Calculus
d) All of the mentioned
Answer: d
Explanation: None.

8. The adjective “first-order” distinguishes first-order logic from ___________ in which there are predicates having predicates or functions as arguments, or in which one or both of predicate quantifiers or function quantifiers are permitted.
a) Representational Verification
b) Representational Adequacy
c) Higher Order Logic
d) Inferential Efficiency
Answer: c
Explanation: None.

9. Which is created by using single propositional symbol?
a) Complex sentences
b) Atomic sentences
c) Composition sentences
d) None of the mentioned
Answer: b
Explanation: Atomic sentences are indivisible syntactic elements consisting of single propositional symbol.

10. Which is used to construct the complex sentences?
a) Symbols
b) Connectives
c) Logical connectives
d) All of the mentioned
Answer: c
Explanation: None.

11. How many proposition symbols are there in artificial intelligence?
a) 1
b) 2
c) 3
d) 4
Answer: b
Explanation: The two proposition symbols are true and false.

12. How many logical connectives are there in artificial intelligence?
a) 2
b) 3
c) 4
d) 5
Answer: d
Explanation: The five logical symbols are negation, conjunction, disjunction, implication and biconditional.

13. Which is used to compute the truth of any sentence?
a) Semantics of propositional logic
b) Alpha-beta pruning
c) First-order logic
d) Both Semantics of propositional logic & Alpha-beta pruning
Answer: a
Explanation: Because the meaning of the sentences is really needed to compute the truth.

14. Which are needed to compute the logical inference algorithm?
a) Logical equivalence
b) Validity
c) Satisfiability
d) All of the mentioned
Answer: d
Explanation: Logical inference algorithm can be solved be using logical equivalence, Validity and satisfiability.

15. From which rule does the modus ponens are derived?
a) Inference rule
b) Module rule
c) Both Inference & Module rule
d) None of the mentioned
Answer: a
Explanation: Inference rule contains the standard pattern that leads to desired goal. The best form of inference rule is modus ponens.

16. Which is also called single inference rule?
a) Reference
b) Resolution
c) Reform
d) None of the mentioned
Answer: b
Explanation: Because resolution yields a complete inference rule when coupled with any search algorithm.

17. Which form is called as a conjunction of disjunction of literals?
a) Conjunctive normal form
b) Disjunctive normal form
c) Normal form
d) All of the mentioned
Answer: a
Explanation: None.

18. What can be viewed as a single lateral of disjunction?
a) Multiple clause
b) Combine clause
c) Unit clause
d) None of the mentioned
Answer: c
Explanation: A single literal can be viewed as a disjunction or one literal also, called a unit clause.

19. Which is a refutation complete inference procedure for propositional logic?
a) Clauses
b) Variables
c) Propositional resolution
d) Proposition
Answer: c
Explanation: Propositional resolution is a refutation complete inference procedure for propositional logic.

20. What kind of clauses are available in Conjunctive Normal Form?
a) Disjunction of literals
b) Disjunction of variables
c) Conjunction of literals
d) Conjunction of variables
Answer: a
Explanation: First-order resolution requires the clause to be in disjunction of literals in Conjunctive Normal Form.

21. What is the condition of literals in variables?
a) Existentially quantified
b) Universally quantified
c) Quantified
d) None of the mentioned
Answer: b
Explanation: Literals that contain variables are assumed to be universally quantified.

22. Which can be converted to inferred equivalent CNF sentence?
a) Every sentence of propositional logic
b) Every sentence of inference
c) Every sentence of first-order logic
d) All of the mentioned
Answer: c
Explanation: Every sentence of first-order logic can be converted to inferred equivalent CNF sentence.

23. Which sentence will be unsatisfiable if the CNF sentence is unsatisfiable?
a) Search statement
b) Reading statement
c) Replaced statement
d) Original statement
Answer: d
Explanation: The CNF statement will be unsatisfiable just when the original sentence is unsatisfiable.

24. Which rule is equal to the resolution rule of first-order clauses?
a) Propositional resolution rule
b) Inference rule
c) Resolution rule
d) None of the mentioned
Answer: a
Explanation: The resolution rule for first-order clauses is simply a lifted version of the propositional resolution rule.

25. At which state does the propositional literals are complementary?
a) If one variable is less
b) If one is the negation of the other
c) All of the mentioned
d) None of the mentioned
Answer: b
Explanation: Propositional literals are complementary if one is the negation of the other.

26. What is meant by factoring?
a) Removal of redundant variable
b) Removal of redundant literal
c) Addition of redundant literal
d) Addition of redundant variable
Answer: b
Explanation: None.

27. What will happen if two literals are identical?
a) Remains the same
b) Added as three
c) Reduced to one
d) None of the mentioned
Answer: c
Explanation: Propositional factoring reduces two literals to one if they are identical.

28. When the resolution is called as refutation-complete?
a) Sentence is satisfiable
b) Sentence is unsatisfiable
c) Sentence remains the same
d) None of the mentioned
Answer: b
Explanation: Resolution is refutation-complete, if a set of sentence is unsatisfiable, then resolution will always be able to derive a contradiction.

29. Which condition is used to cease the growth of forward chaining?
a) Atomic sentences
b) Complex sentences
c) No further inference
d) All of the mentioned
Answer: c
Explanation: Forward chain can grow by adding new atomic sentences until no further inference is made.

30. Which closely resembles propositional definite clause?
a) Resolution
b) Inference
c) Conjunction
d) First-order definite clauses
Answer: d
Explanation: Because they are disjunction of literals of which exactly one is positive.

31. What is the condition of variables in first-order literals?
a) Existentially quantified
b) Universally quantified
c) Both Existentially & Universally quantified
d) None of the mentioned
Answer: b
Explanation: First-order literals will accept variables only if they are universally quantified.

32. Which are more suitable normal form to be used with definite clause?
a) Positive literal
b) Negative literal
c) Generalized modus ponens
d) Neutral literal
Answer: c
Explanation: Definite clauses are a suitable normal form for use with generalized modus ponen.

33. Which will be the instance of the class datalog knowledge bases?
a) Variables
b) No function symbols
c) First-order definite clauses
d) None of the mentioned
Answer: b
Explanation: If the knowledge base contains no function symbols means, it is an instance of the class datalog knowledge base.

34. Which knowledge base is called as fixed point?
a) First-order definite clause are similar to propositional forward chaining
b) First-order definite clause are mismatch to propositional forward chaining
c) All of the mentioned
d) None of the mentioned
Answer: a
Explanation: Fixed point reached by forward chaining with first-order definiteclause are similar to those for propositional forward chaining.

35. How to eliminate the redundant rule matching attempts in the forward chaining?
a) Decremental forward chaining
b) Incremental forward chaining
c) Data complexity
d) None of the mentioned
Answer: b
Explanation: We can eliminate the redundant rule matching attempts in the forward chaining by using incremental forward chaining.

36. From where did the new fact inferred on new iteration is derived?
a) Old fact
b) Narrow fact
c) New fact
d) All of the mentioned
Answer: c
Explanation: None.

37. Which will solve the conjuncts of the rule so that the total cost is minimized?
a) Constraint variable
b) Conjunct ordering
c) Data complexity
d) All of the mentioned
Answer: b
Explanation: Conjunct ordering will find an ordering to solve the conjuncts of the rule premise so that the total cost is minimized.

38. How many possible sources of complexity are there in forward chaining?
a) 1
b) 2
c) 3
d) 4
Answer: c
Explanation: The three possible sources of complexity are an inner loop, algorithm rechecks every rule on every iteration, algorithm might generate many facts irrelevant to the goal.

39. Which algorithm will work backward from the goal to solve a problem?
a) Forward chaining
b) Backward chaining
c) Hill-climb algorithm
d) None of the mentioned
Answer: b
Explanation: Backward chaining algorithm will work backward from the goal and it will chain the known facts that support the proof.

40. Which is mainly used for automated reasoning?
a) Backward chaining
b) Forward chaining
c) Logic programming
d) Parallel programming
Answer: c
Explanation: Logic programming is mainly used to check the working process of the system.

41. What will backward chaining algorithm will return?
a) Additional statements
b) Substitutes matching the query
c) Logical statement
d) All of the mentioned
Answer: b
Explanation: It will contains the list of goals containing a single element and returns the set of all substitutions satisfying the query.

42. How can be the goal is thought of in backward chaining algorithm?
a) Queue
b) List
c) Vector
d) Stack
View Answer
Answer: d
Explanation: The goals can be thought of as stack and if all of them us satisfied means, then current branch of proof succeeds.

43. What is used in backward chaining algorithm?
a) Conjuncts
b) Substitution
c) Composition of substitution
d) None of the mentioned
Answer: c
Explanation: None.

44. Which algorithm are in more similar to backward chaining algorithm?
a) Depth-first search algorithm
b) Breadth-first search algorithm
c) Hill-climbing search algorithm
d) All of the mentioned
Answer: a
Explanation: It is depth-first search algorithm because its space requirements are linear in the size of the proof.

45. Which problem can frequently occur in backward chaining algorithm?
a) Repeated states
b) Incompleteness
c) Complexity
d) Both Repeated states & Incompleteness
Answer: d
Explanation: If there is any loop in the chain means, It will lead to incompleteness and repeated states.

46. How the logic programming can be constructed?
a) Variables
b) Expressing knowledge in a formal language
c) Graph
d) All of the mentioned
Answer: b
Explanation: Logic programming can be constructed by expressing knowledge in a formal expression and the problem can be solved by running inference process.

47. What form of negation does the prolog allows?
a) Negation as failure
b) Proposition
c) Substitution
d) Negation as success
Answer: a
Explanation: None.

48. Which is omitted in prolog unification algorithm?
a) Variable check
b) Occur check
c) Proposition check
d) Both Occur & Proposition check
Answer: b
Explanation: Occur check is omitted in prolog unification algorithm because of unsound inferences.

49. Knowledge and reasoning also play a crucial role in dealing with __________________ environment.
a) Completely Observable
b) Partially Observable
c) Neither Completely nor Partially Observable
d) Only Completely and Partially Observable
Answer: b
Explanation: Knowledge and reasoning could aid to reveal other factors that could complete environment.

50. Treatment chosen by doctor for a patient for a disease is based on _____________
a) Only current symptoms
b) Current symptoms plus some knowledge from the textbooks
c) Current symptoms plus some knowledge from the textbooks plus experience
d) All of the mentioned
Answer: c
Explanation: None.

51. A knowledge-based agent can combine general knowledge with current percepts to infer hidden aspects of the current state prior to selecting actions.
a) True
b) False
Answer: a
Explanation: Refer definition of Knowledge based agents.

52. A) Knowledge base (KB) is consists of set of statements.
B) Inference is deriving a new sentence from the KB.
Choose the correct option.
a) A is true, B is true
b) A is false, B is false
c) A is true, B is false
d) A is false, B is true
Answer: a
Explanation: None.

53. Wumpus World is a classic problem, best example of _______
a) Single player Game
b) Two player Game
c) Reasoning with Knowledge
d) Knowledge based Game
Answer: c
Explanation: Refer the definition of Wumpus World Problem.

54. ‘α |= β ‘(to mean that the sentence α entails the sentence β) if and only if, in every model in which α is _____ β is also _____
a) True, true
b) True, false
c) False, true
d) False, false
Answer: a
Explanation: Refer the definition of law of entailment.

55. Which is not a property of representation of knowledge?
a) Representational Verification
b) Representational Adequacy
c) Inferential Adequacy
d) Inferential Efficiency
Answer: a
Explanation: None.

56. Which is not Familiar Connectives in First Order Logic?
a) and
b) iff
c) or
d) not
Answer: d
Explanation: “not” is coming under propositional logic and is therefore not a connective.

57. Inference algorithm is complete only if _____________
a) It can derive any sentence
b) It can derive any sentence that is an entailed version
c) It is truth preserving
d) It can derive any sentence that is an entailed version & It is truth preserving

58. An inference algorithm that derives only entailed sentences is called sound or truth-preserving.
a) True
b) False
Answer: a
Explanation: None.

59. The rule of Universal Instantiation (UI for short) says that we can infer any sentence obtained by substituting a ground term (a term without variables) for the variable.
a) True
b) False
Answer: a
Explanation: Rule of universal instantiation.

60. The corresponding Existential Instantiation rule: for the existential quantifier is slightly more complicated. For any sentence a, variable v, and constant symbol k that does not appear elsewhere in the knowledge base.
a) True
b) False
Answer: a
Explanation: Rule of existential instantiation.

61. What among the following could the universal instantiation of ___________
For all x King(x) ^ Greedy(x) => Evil(x)
a) King(John) ^ Greedy(John) => Evil(John)
b) King(y) ^ Greedy(y) => Evil(y)
c) King(Richard) ^ Greedy(Richard) => Evil(Richard)
d) All of the mentioned
Answer: d
Explanation: Refer the definition if universal instantiation.

62. Lifted inference rules require finding substitutions that make different logical expressions looks identical.
a) Existential Instantiation
b) Universal Instantiation
c) Unification
d) Modus Ponen
Answer: c
Explanation: None.

63. Which of the following is not the style of inference?
a) Forward Chaining
b) Backward Chaining
c) Resolution Refutation
d) Modus Ponen
Answer: d
Explanation: Modus ponen is a rule for an inference.

64. In order to utilize generalized Modus Ponens, all sentences in the KB must be in the form of Horn sentences.
a) True
b) False
Answer: a
Explanation: None.

65. For resolution to apply, all sentences must be in conjunctive normal form, a conjunction of disjunctions of literals.
a) True
b) False
Answer: a
Explanation: None.

66. What are the two basic types of inferences?
a) Reduction to propositional logic, Manipulate rules directly
b) Reduction to propositional logic, Apply modus ponen
c) Apply modus ponen, Manipulate rules directly
d) Convert every rule to Horn Clause, Reduction to propositional logic
Answer: a
Explanation: None.

67. Which among the following could the Existential instantiation of ∃x Crown(x) ^ OnHead(x, Johnny)?
a) Crown(John) ^ OnHead(John, Jonny)
b) Crown(y) ^ OnHead(y, y, x)
c) Crown(x) ^ OnHead(x, Jonny)
d) None of the mentioned
Answer: a
Explanation: None.

68. Translate the following statement into FOL.
“For every a, if a is a PhD student, then a has a master degree”
a) ∀ a PhD(a) -> Master(a)
b) ∃ a PhD(a) -> Master(a)
c) A is true, B is true
d) A is false, B is false
Answer: a
Explanation: None

69. The rule of Universal Instantiation (UI for short) says that we can infer any sentence obtained by substituting a ground term (a term without variables) for the variable.
a) True
b) False
Answer: a
Explanation: Rule of universal instantiation.

70. The corresponding Existential Instantiation rule: for the existential quantifier is slightly more complicated. For any sentence a, variable v, and constant symbol k that does not appear elsewhere in the knowledge base.
a) True
b) False
Answer: a
Explanation: Rule of existential instantiation.

1. What is Fuzzy Logic?
A. a method of reasoning that resembles human reasoning
B. a method of question that resembles human answer
C. a method of giving answer that resembles human answer.
D. None of the Above
View Answer
Ans : A
Explanation: Fuzzy Logic (FL) is a method of reasoning that resembles human reasoning.

2. How many output Fuzzy Logic produce?
A. 2
B. 3
C. 4
D. 5
View Answer
Ans : A
Explanation: The conventional logic block that a computer can understand takes precise input and produces a definite output as TRUE or FALSE, which is equivalent to human’s YES or NO.

3. Fuzzy Logic can be implemented in?
A. Hardware
B. software
C. Both A and B
D. None of the Above
View Answer
Ans : C
Explanation: It can be implemented in hardware, software, or a combination of both.

4. The truth values of traditional set theory is ____________ and that of fuzzy set is __________
A. Either 0 or 1, between 0 & 1
B. Between 0 & 1, either 0 or 1
C. Between 0 & 1, between 0 & 1
D. Either 0 or 1, either 0 or 1
View Answer
Ans : A
Explanation: Refer the definition of Fuzzy set and Crisp set.

5. How many main parts are there in Fuzzy Logic Systems Architecture?
A. 3
B. 4
C. 5
D. 6
View Answer
Ans : B
Explanation: It has four main parts.

6. Each element of X is mapped to a value between 0 and 1. It is called _____.
A. membership value
B. degree of membership
C. membership value
D. Both A and B
View Answer
Ans : D
Explanation: each element of X is mapped to a value between 0 and 1. It is called membership value or degree of membership.

7. How many level of fuzzifier is there?
A. 4
B. 5
C. 6
D. 7
View Answer
Ans : B
Explanation: There is 5 level to fuzzifier

8. Fuzzy Set theory defines fuzzy operators. Choose the fuzzy operators from the following.
A. AND
B. OR
C. NOT
D. All of the above
View Answer
Ans : D
Explanation: The AND, OR, and NOT operators of Boolean logic exist in fuzzy logic, usually defined as the minimum, maximum, and complement;

9. The room temperature is hot. Here the hot (use of linguistic variable is used) can be represented by _______
A. Fuzzy Set
B. Crisp Set
C. Both A and B
D. None of the Above
View Answer
Ans : A
Explanation: Fuzzy logic deals with linguistic variables.

10. What action to take when IF (temperature=Warm) AND (target=Warm) THEN?
A. Heat
B. No_Change
C. Cool
D. None of the Above
View Answer
Ans : B
Explanation: IF (temperature=Warm) AND (target=Warm) THEN No_change

11. What is the form of Fuzzy logic?
a) Two-valued logic
b) Crisp set logic
c) Many-valued logic
d) Binary set logic
View Answer Answer: c
Explanation: With fuzzy logic set membership is defined by certain value. Hence it could have many values to be in the set.

12. Traditional set theory is also known as Crisp Set theory.
a) True
b) False
View Answer Answer: a
Explanation: Traditional set theory set membership is fixed or exact either the member is in the set or not. There is only two crisp values true or false. In case of fuzzy logic there are many values. With weight say x the member is in the set.

13. The truth values of traditional set theory is ____________ and that of fuzzy set is __________
a) Either 0 or 1, between 0 & 1
b) Between 0 & 1, either 0 or 1
c) Between 0 & 1, between 0 & 1
d) Either 0 or 1, either 0 or 1
View Answer Answer: a
Explanation: Refer the definition of Fuzzy set and Crisp set.

14. Fuzzy logic is extension of Crisp set with an extension of handling the concept of Partial Truth.
a) True
b) False
View Answer Answer: a
Explanation: None.

15. The room temperature is hot. Here the hot (use of linguistic variable is used) can be represented by _______
a) Fuzzy Set
b) Crisp Set
c) Fuzzy & Crisp Set
d) None of the mentioned
View Answer Answer: a
Explanation: Fuzzy logic deals with linguistic variables.

16. The values of the set membership is represented by ___________
a) Discrete Set
b) Degree of truth
c) Probabilities
d) Both Degree of truth & Probabilities
View Answer Answer: b
Explanation: Both Probabilities and degree of truth ranges between 0 – 1.

17. Japanese were the first to utilize fuzzy logic practically on high-speed trains in Sendai.
a) True
b) False
View Answer Answer: a
Explanation: None.

18. Fuzzy Set theory defines fuzzy operators. Choose the fuzzy operators from the following.
a) AND
b) OR
c) NOT
d) All of the mentioned
View Answer Answer: d
Explanation: The AND, OR, and NOT operators of Boolean logic exist in fuzzy logic, usually defined as the minimum, maximum, and complement;

19. There are also other operators, more linguistic in nature, called __________ that can be applied to fuzzy set theory.
a) Hedges
b) Lingual Variable
c) Fuzz Variable
d) None of the mentioned
View Answer Answer: a
Explanation: None.

20. Fuzzy logic is usually represented as ___________
a) IF-THEN-ELSE rules
b) IF-THEN rules
c) Both IF-THEN-ELSE rules & IF-THEN rules
d) None of the mentioned
View Answer Answer: b
Explanation: Fuzzy set theory defines fuzzy operators on fuzzy sets. The problem in applying this is that the appropriate fuzzy operator may not be known. For this reason, fuzzy logic usually uses IF-THEN rules, or constructs that are equivalent, such as fuzzy associative matrices.
Rules are usually expressed in the form:
IF variable IS property THEN action

21. Like relational databases there does exists fuzzy relational databases.
a) True
b) False
View Answer Answer: a
Explanation: Once fuzzy relations are defined, it is possible to develop fuzzy relational databases. The first fuzzy relational database, FRDB, appeared in Maria Zemankova dissertation.

22. ______________ is/are the way/s to represent uncertainty.
a) Fuzzy Logic
b) Probability
c) Entropy
d) All of the mentioned
View Answer Answer: d
Explanation: Entropy is amount of uncertainty involved in data. Represented by H(data).

23. ____________ are algorithms that learn from their more complex environments (hence eco) to generalize, approximate and simplify solution logic.
a) Fuzzy Relational DB
b) Ecorithms
c) Fuzzy Set
d) None of the mentioned
View Answer Answer: c
Explanation: Local structure is usually associated with linear rather than exponential growth in complexity.

24. Membership function defines the fuzziness in a fuzzy set irrespective of the elements in the set, which are discrete or continuous.
a.) True
b.) False
Answer: A

25.The membership functions are generally represented in
a.) Tabular form
b) Graphical form
c) Mathematical form
d) Logical form
Ans: B

26.Membership function can be thought of as a technique to solve empirical problems on the basis of
a) knowledge
b) example
c) learning
d) experience
Ans: D

27.Three main basic features involved in characterizing membership function are
a)Intution, Inference, Rank Ordering
b)Fuzzy Algorithm, Neural network, Genetic Algorithm
c)Core, Support , Boundary
d)Weighted Average, center of Sums, Median
Ans : C

28. A fuzzy set whose membership function has at least one element x in the universe whose membership value
is unity is called
a) sub normal fuzzy sets
b) normal fuzzy set
c) convex fuzzy set
d) concave fuzzy set
Ans: B

29. In a Fuzzy set a prototypical element has a value
a) 1
b) 0
c) infinite
d) not defined
Ans: A

30. A fuzzy set wherein no membership function has its value equal to 1 is called
a) Normal fuzzy set
b) Sub normal fuzzy set
c) convex fuzzy set
d) non convex fuzzy set
Ans: B

31.A fuzzy set has a membership function whose membership values are strictly monotonically increasing or strictly monotonically decreasing or strictly monotonically increasing than strictly monotonically decreasing with increasing values for elements in the universe
a) Convex fuzzy set
b) Concave fuzzy set
c) Non Concave fuzzy set
d) Non Convex fuzzy set
Ans : A

32. The membership values of the membership function are nor strictly monotonically increasing or decreasing or strictly monoronically increasing than decreasing.
a) Convex fuzzy set
b) non-convex fuzzy set
c) normal fuzzy set
d) subnormal fuzzy set
Ans: B

1. Who was the inventor of the first neurocomputer?
A. Dr. John Hecht-Nielsen
B. Dr. Robert Hecht-Nielsen
C. Dr. Alex Hecht-Nielsen
D. Dr. Steve Hecht-Nielsen
Ans : B
Explanation: The inventor of the first neurocomputer, Dr. Robert Hecht-Nielsen.

2. How many types of Artificial Neural Networks?
A. 2
B. 3
C. 4
D. 5
Ans : A
Explanation: There are two Artificial Neural Network topologies : FeedForward and Feedback.

3. In which ANN, loops are allowed?
A. FeedForward ANN
B. FeedBack ANN
C. Both A and B
D. None of the Above
Ans : B
Explanation: FeedBack ANN loops are allowed. They are used in content addressable memories.

4. What is the full form of BN in Neural Networks?
A. Bayesian Networks
B. Belief Networks
C. Bayes Nets
D. All of the above
Ans : D
Explanation: The full form BN is Bayesian networks and Bayesian networks are also called Belief Networks or Bayes Nets.

5. What is the name of node which take binary values TRUE (T) and FALSE (F)?
A. Dual Node
B. Binary Node
C. Two-way Node
D. Ordered Node
Ans : B
Explanation: Boolean nodes : They represent propositions, taking binary values TRUE (T) and FALSE (F).

6. What is an auto-associative network?
A. a neural network that contains no loops
B. a neural network that contains feedback
C. a neural network that has only one loop
D. a single layer feed-forward neural network with pre-processing
Ans : B
Explanation: An auto-associative network is equivalent to a neural network that contains feedback. The number of feedback paths(loops) does not have to be one.

7. What is Neuro software?
A. A software used to analyze neurons
B. It is powerful and easy neural network
C. Designed to aid experts in real world
D. It is software used by Neurosurgeon
Ans : B
Explanation: Neuro software is powerful and easy neural network.

8. Neural Networks are complex ______________ with many parameters.
A. Linear Functions
B. Nonlinear Functions
C. Discrete Functions
D. Exponential Functions
Ans : A
Explanation: Neural networks are complex linear functions with many parameters.

9. Which of the following is not the promise of artificial neural network?
A. It can explain result
B. It can survive the failure of some nodes
C. It has inherent parallelism
D. It can handle noise
Ans : A
Explanation: The artificial Neural Network (ANN) cannot explain result.

10. The output at each node is called_____.
A. node value
B. Weight
C. neurons
D. axons
Ans : A
Explanation: The output at each node is called its activation or node value.

11.ANN is composed of large number of highly interconnected processing elements(neurons) working in unison to solve problems.
True
False
Ans : A

12:Artificial neural network used for
A.Pattern Recognition
B.Classification
C.Clustering
D.All of these
Ans : D

13:A Neural Network can answer
A.For Loop questions
B.what-if questions
C.IF-The-Else Analysis Questions
D.None of these
Ans : B

14:Ability to learn how to do tasks based on the data given for training or initial experience
A.Self Organization
B.Adaptive Learning
C.Fault tolerance
D.Robustness
Ans : B

15: Feature of ANN in which ANN creates its own organization or representation of information it receives during learning time is
A.Adaptive Learning
B.Self Organization
C.What-If Analysis
D.Supervised Learniing
Ans : B

16: In artificial Neural Network interconnected processing elements are called
A.nodes or neurons
B.weights
C.axons
D.Soma
Ans:A

17: Each connection link in ANN is associated with ________ which has information about the input signal.
A.neurons
B.weights
C.bias
D.activation function
Ans : B

18.Neurons or artificial neurons have the capability to model networks of original neurons as found in brain
A.True
B.False
Ans : A

19.Internal state of neuron is called __________, is the function of the inputs the neurons receives
A.Weight
B.activation or activity level of neuron
C.Bias
D.None of these
Ans: B

20. Neuron can send ________ signal at a time.
A.multiple
B.one
C.none
D.any number of
Ans: B

1. Which university introduced Expert systems ?
A. Massachusetts Institute of Technology
B. University of Oxford
C. Stanford University
D. University of Cambridge
Ans : C
Explanation: Expert System introduced by the researchers at Stanford University, Computer Science Department.

2. Which of the following is not a Capabilities of Expert Systems?
A. Advising
B. Demonstrating
C. Explaining
D. Expanding
Ans : D
Explanation: Expanding is not Capabilities of Expert Systems.

3. Which of the following are Components of Expert Systems?
A. Knowledge Base
B. Inference Engine
C. User Interface
D. All of the above
Ans : D
Explanation: The components of ES include : Knowledge Base, Inference Engine, User Interface.

4. Which of the following is incorrect application of Expert System?
A. Design Domain
B. Monitoring Systems
C. Knowledge Domain
D. Systems domain
Ans : D
Explanation: Systems domain is incorrect application of Expert System

5. In LISP, the function returns t if is even and nil otherwise ___________+
A. (evenp <integer>)
B. (even <integer>)
C. (numeven <integer>)
D. (numnevenp <integer>)
Ans : A
Explanation: In LISP, the function returns t if <integer> is even and nil otherwise (evenp <integer>)

6. The “Turing Machine” showed that you could use a/an _____ system to program any algorithmic task.
A. binary
B. electro-chemical
C. recursive
D. semantic
Ans : A
Explanation: The “Turing Machine” showed that you could use a/an binary system to program any algorithmic task.

7. Input segments of AI programming contain(s)?
A. Sound
B. Smell
C. Touch
D. None of the Above
Ans : D
Explanation: Input segments of AI programming contain(s) sounds, smell, touch.

8. Which of the following is not a benefits of Expert Systems?
A. Availability
B. Speed
C. Time
D. Less Error Rate
Ans : C
Explanation: Time is not Benefits of Expert Systems.

9. What is the full form of JESS in Expert System Technology?
A. Java Expert System Shell
B. Javascript Expert System Shell
C. Java Expert Sub System
D. Javascript Expert Sub System
Ans : A
Explanation: Java Expert System Shell (JESS) that provides fully developed Java API for creating an expert system.

10. What is the form of Knowledge representation?
A. IF-THEN
B. IF-THEN-ELSE
C. IF-ELSE
D. All of the above
Ans : B
Explanation: It is the method used to organize and formalize the knowledge in the knowledge base. It is in the form of IF-THEN-ELSE rules.