Artificial Intelligence Questions and Answers Set 2

Artificial Intelligence

Questions 11 to 20

11.
What are you predicating by the logic: ۷x: €y: loyalto(x, y).
(a)  Everyone is loyal to some one           (b)  Everyone is loyal to all
(c)  Everyone is not loyal to someone       (d)  Everyone is loyal
(e)  Everyone is not loyal.
12.
Which is not Familiar Connectives in First Order Logic?
(a)  and                   (b)  iff                      (c)  or                      (d)  not                    (e)  either a or b.
13.
Which is not a type of First Order Logic (FOL) Sentence?
(a)  Atomic sentences                                                           (b)  Complex sentences
(c)  Quantified sentence                          (d)  Quality Sentence
(e)  Simple sentence.
14.
Which is not a Goal-based agent?
(a)  Inference                                          (b)  Search                                             (c)  Planning
(d)  Conclusion                                       (e)  Dynamic search.
15.
A plan that describe how to take actions in levels of increasing refinement and specificity is
(a)  Problem solving                                (b)  Planning
(c)  Non-hierarchical plan                         (d)  Hierarchical plan (e)  Inheritance.
16.
A constructive approach in which no commitment is made unless it is necessary to do so, is
(a)  Least commitment approach              (b)  Most commitment approach
(c)  Nonlinear planning                            (d)  Opportunistic planning
(e)  Problem based planning.
17.
Partial order planning involves
(a)   Searching over the space of possible plans
(b)   Searching over possible situations
(c)   Searching the whole problem at once
(d)   Searching the best
(e)   Searching the goal.
18.
Which is true for Decision theory?
(a)   Decision Theory = Probability theory + utility theory
(b)   Decision Theory = Inference theory + utility theory
(c)   Decision Theory = Uncertainty + utility theory
(d)   Decision Theory = Probability theory + preference
(e)   Decision Theory = Probability theory + inference.
19.
Uncertainty arises in the wumpus world because the agent’s sensors give only
(a)  Full & Global information                   (b)  Partial & Global Information
(c)  Partial & local Information                  (d)  Full & local information
(e)  Global information only.
20.
A Hybrid Bayesian network contains
(a)   Both discrete and continuous variables
(b)   Only Discrete variables
(c)   Only Discontinuous variable
(d)   Both Discrete and Discontinuous variable
(e)   Continous variable only.


Answers


11.
Answer : (a)
Reason : ۷x denotes Everyone or all, and €y someone and loyal to is the proposition logic making map x to y.
12.
Answer : (d)
Reason : “not” is coming under propositional logic and is therefore not a connective.
13.
Answer : (d)
Reason : Quantity structure is not a FOL structure while all other are.
14.
Answer : (d)
Reason : Conclusion is a statement to Goal-based agent, but is not considered as Goal-based agent.
15.
Answer : (d)
Reason : A plan that describes how to take actions in levels of increasing refinement and specificity is Hierarchical (e.g., "Do something" becomes the more specific "Go to work," "Do work," "Go home.") Most plans are hierarchical in nature.
16.
Answer : (a)
Reason : Because we are not sure about the outcome.
17.
Answer : (a)
Reason : Partial order planning involves searching over the space of possible plans, rather than searching over possible situations. The idea is to construct a plan piece-by-piece. There are two kinds of steps we can take in constructing a plan: add an operator (action), or add an ordering constraint between operators. The name "partial order planning" comes from the fact that until we add the ordering constraints, we don't specify the order in which actions are taken. This (sometimes) allows a partial order planning to avoid lots of backtracking that would slow down a state-space planner.
18.
Answer : (a)
Reason : Utility theory to represent and reason with preference. Preference is expressed by utilities. Utilities are combined with probabilities in the general theory of rational decisions called decision theory. Decision theory, which combines probability theory with utility theory, provides a formal and complete frame work for decisions (economic or otherwise) made under uncertainty-that is, in case where probabilistic descriptions appropriately capture the decision-maker’s environment.
19.
Answer : (c)
Reason : The Wumpus world is a grid of squares surrounded by walls, where each square can contain agents and objects. The agent (you) always starts in the lower left corner, a square that will be labeled [1, 1]. The agent's task is to find the gold, return to [1, 1] and climb out of the cave. So uncertainty is there as the agent gives partial and local information only. Global variable are not goal specific problem solving.
20.
Answer : (a)
Reason : To specify a Hybrid network, we have to specify two new kinds of distributions: the conditional distribution for continuous variables given discrete or continuous parents, and the conditional distribution for a discrete variable given continuous parents.


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2 comments :

  1. Anthony, Bob, Christina, and Diane sit around a table that has four seats: North,
    East, South and West. We have the following information:
    1. Anthony is seated south.
    2. Diane sits opposite to Bob.
    3. Exactly one of Bob’s neighbors has a glass.
    4. A glass stands on the table positioned north.
    5. If Diane has a glass then her two neighbors have a glass as well.
    6. The neighbor to Anthony’s right (from his point of view) has a glass.
    You have the task to model the given situation with the help of formulas of
    propositional logic. Use the following variables:
    {xy|x 2 {A,B,C,D}, y 2 {N,O, S,W}} = {AN,AO,AS,AW,BN, ...,DS,DW}
    For example, the variable AN is true, if and only if Anthony is seated North
    of the table. Furthermore, the variables {A,B,C,D,N,O, S,W} can be used to
    describe the position of the glasses: The variables A ... D are true, if and only if
    there is a glass in front of person A ... D, respectively. The variables N ... W are
    true, if and only if there is a glass at the respective position on the table.
    Write formulas of propositional logic to model the information (1) to (6) given
    above. (Remember that you can use implications!)
    Task 7
    Resolution
    Knowledge: W = (X _ F _ ¬M _ R) ^ (¬F _ X _ B) ^ (¬R _ ¬F _ X _ ¬B) ^
    (X _ F _M) ^ (X _ ¬M _ ¬R _ F) ^ (¬F _ X _ R _ ¬A _ ¬B) ^ ¬X
    Hypothesis: H = ¬(¬F _ X _ ¬B _ R _ A)
    Question: Does W |= H hold?
    Apply the resolution method! (Note that the hypothesis is already nearly in
    CNF; just remove the first negation and start the resolution.)

    ReplyDelete
  2. plz solve me these two questions iam confuse plz help me post the solution of these two tasks in artificial intelligence

    Anthony, Bob, Christina, and Diane sit around a table that has four seats: North,
    East, South and West. We have the following information:
    1. Anthony is seated south.
    2. Diane sits opposite to Bob.
    3. Exactly one of Bob’s neighbors has a glass.
    4. A glass stands on the table positioned north.
    5. If Diane has a glass then her two neighbors have a glass as well.
    6. The neighbor to Anthony’s right (from his point of view) has a glass.
    You have the task to model the given situation with the help of formulas of
    propositional logic. Use the following variables:
    {xy|x 2 {A,B,C,D}, y 2 {N,O, S,W}} = {AN,AO,AS,AW,BN, ...,DS,DW}
    For example, the variable AN is true, if and only if Anthony is seated North
    of the table. Furthermore, the variables {A,B,C,D,N,O, S,W} can be used to
    describe the position of the glasses: The variables A ... D are true, if and only if
    there is a glass in front of person A ... D, respectively. The variables N ... W are
    true, if and only if there is a glass at the respective position on the table.
    Write formulas of propositional logic to model the information (1) to (6) given
    above. (Remember that you can use implications!)
    Task 7
    Resolution
    Knowledge: W = (X _ F _ ¬M _ R) ^ (¬F _ X _ B) ^ (¬R _ ¬F _ X _ ¬B) ^
    (X _ F _M) ^ (X _ ¬M _ ¬R _ F) ^ (¬F _ X _ R _ ¬A _ ¬B) ^ ¬X
    Hypothesis: H = ¬(¬F _ X _ ¬B _ R _ A)
    Question: Does W |= H hold?
    Apply the resolution method! (Note that the hypothesis is already nearly in
    CNF; just remove the first negation and start the resolution.)

    ReplyDelete