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Artificial Intelligence: Uncertain Knowledge and Reasoning
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Artificial Intelligence: Uncertain Knowledge and Reasoning
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25 Questions

1. How many types of random variables are available?
2. How many formal languages are used for stating propositions?
3. The primitives in probabilistic reasoning are random variables.
4. Which variable cannot be written in entire distribution as a table?
5. How many types of quantification are available in artificial intelligence?
6. Which condition is used to influence a variable directly by all the others?
7. How the compactness of the bayesian network can be described?
8. How is Fuzzy Logic different from conventional control methods?
9. Which is true for Decision theory?
10. If a hypothesis says it should be positive, but in fact it is negative, we call it ___________
11. What is the basic element of a language?
12. A Hybrid Bayesian network contains ___________
13. How many terms are required for building a bayes model?
14. Using logic to represent and reason we can represent knowledge about the world with facts and rules.
15. What is meant by probability density function?
16. How the bayesian network can be used to answer any query?
17. What is the extraction of the meaning of utterance?
18. What is the consequence between a node and its predecessors while creating bayesian network?
19. What kind of interpretation is done by adding context-dependant information?
20. What does the bayesian network provides?
21. What is needed to make probabilistic systems feasible in the world?
22. Uncertainty arises in the wumpus world because the agent's sensors give only ___________
23. What is used for probability theory sentences?
24. A constructive approach in which no commitment is made unless it is necessary to do so is ___________
25. What is meant by quasi-logical form?