1. Using logic to represent and reason we can represent knowledge about the world with facts and rules,
2. 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
Explanation: 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.
3. 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
Explanation: 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.
4. How is Fuzzy Logic different from conventional control methods?
a) IF and THEN Approach
b) FOR Approach
c) WHILE Approach
d) DO Approach
Explanation: FL incorporates a simple, rule-based IF X AND Y THEN Z approach to a solving control problem rather than attempting to model a system mathematically.
5. If a hypothesis says it should be positive, but in fact it is negative, we call it
a) A consistent hypothesis
b) A false negative hypothesis
c) A false positive hypothesis
d) A specialized hypothesis
Explanation: Consistent hypothesis go with examples, If the hypothesis says it should be negative but infact it is positive, it is false negative. If a hypothesis says it should be positive, but in fact it is negative, it is false positive. In a specialized hypothesis we need to have certain restrict or special conditions.
6. The primitives in probabilistic reasoning are random variables.
Explanation: The primitives in probabilistic reasoning are random variables. Just like primitives in Propositional Logic are propositions. A random variable is not in fact a variable, but a function from a sample space S to another space, often the real numbers.
7. What is used for probability theory sentences?
a) Conditional logic
c) Extension of propositional logic
d) None of the mentioned
Explanation: The version of probability theory we present uses an extension of propositional logic for its sentences.
8. Where does the dependence of experience is reflected in prior probability sentences?
a) Syntactic distinction
b) Semantic distinction
c) Both a & b
d) None of the mentioned
Explanation: The dependence on experience is reflected in the syntactic distinction between prior probability statements.
9. Where does the degree of belief are applied?
10. How many formal languages are used for stating propositions?
Explanation: The two formal languages used for stating propositions are propositional logic and first- order logic.
Sanfoundry Global Education & Learning Series – Artificial Intelligence.