Artificial intelligence

Define satisfiability of a sentence.

Why does uncertainty arise?

What are not represented by using propositional logic?

(i) Objects
(ii) Relations
(iii) Both objects and relations
(iv) None of the above

What is meant by belief network?

A knowledge-based agent can combine general knowledge with current percepts to infer hidden aspects of the current state prior to selecting actions.

(i) True
(ii) False

Inference algorithm is completed only if

(i) it can derive any sentence
(ii) it can derive any sentence that is an entailed version
(iii) it is truth preserving
(iv) it can derive any sentence that is an entailed version and it is truth preserving

Propositional logic cannot express which of the following?

(i) True/false statements
(ii) Compound statements
(iii) Logical connectives
(iv) Variables and functions

What are the two basic types of inference?

(i) Reduction to propositional logic, manipulate rules directly
(ii) Reduction to propositional logic, apply modus ponen
(iii) Apply modus ponen, manipulate rules directly
(iv) Convert every rule of horn clause, reduction to propositional logic

Bayesian networks are especially useful in situations with:

(i) Complete knowledge and logic
(ii) No uncertainty
(iii) Deterministic processes
(iv) Probabilistic and uncertain information

Bayesian Networks are used to handle

(i) Certain knowledge
(ii) Uncertain knowledge
(iii) Deterministic knowledge
(iv) Syntax only

What is an atomic event?

What is the need for utility theory in uncertainty?

Graph used to represent semantic network is

(i) undirected graph
(ii) directed graph
(iii) directed acyclic graph (DAG)
(iv) directed complete graph

What are the steps to convert first order logic or predicate logic sentence to normal form. Explain each step.

Draw the semantic network of the following sentence:

Kavita gives a book to her friend.

Consider this knowledge-base (KB) for an instance of the Blocks World problem :

Query : $\exists w$ above $(w, B)$?

KB for Blocks World problem :

1. on (A, C)
2. on (D, B)

Consider the knowledge-base (KB) for the Blocks World problem from the previous question.

Draw the complete AND-OR proof tree showing all the answers to the query ∃w above(w, B).

Define prior probability and conditional probability. State Bayes's theorem. How is it useful for decision making under uncertainty?

Explain logics for non-monotonic reasoning.

Compare probabilistic reasoning using Bayesian Networks with logical reasoning. In what situations is probabilistic reasoning more appropriate? Give examples.

What is First Order Predicate Logic (FOPL)? Represent the following facts in FOPL:

"Anyone passing his AI paper and getting an opportunity to work on live project is Happy. But anyone who studies sincerely or is Lucky can pass all his exams. Ramu did not study but he is Lucky. Anyone who is Lucky gets a live project to work."

Write down the difference between Forward reasoning and Backward reasoning.

What is fuzzy set? What is the difference between fuzzy set and crisp set? Explain different fuzzy set operations using examples.

Write short notes on the following:

Knowledge

What is partial-order planning? Explain with a block-world example, showing how actions can be arranged without a strict linear order.

Discuss the role of Probabilistic reasoning in handling uncertainty.

Write a Prolog program that verified whether an input list is a palindrome.

Hint:
Goal : Palindrome ([r, a, c, e, c, a, r])
Output : Yes
Goal : Palindrome ([a, b, c])
Output : No

Explain resolution in First-Order Logic (FOL). Using a simple knowledge base (e.g., "All humans are mortal, Socrates is a human"), derive a conclusion using resolution refutation.

Write short notes on the following:

Inheritance knowledge

Write short notes on the following:

Knowledge management

Explain Bayesian Networks and their role in handling uncertainty.

Convert the following statements into First Order Logic: "All men are mortal. Socrates is a man."

Differentiate between forward chaining and backward chaining. On what factors does the decision to choose forward or backward chaining depend?

Explain Semantic network with suitable example.

Consider the following sentences:

• John likes all kinds of food.
• Apples are food.
• Chicken is food.
• Anything anyone eats and isn't killed by it, is food.
• Sue eats everything Bill eats.

(i) Translate the sentences into formulae in predicate logic. Prove that John likes peanuts using backward chaining.
(ii) Convert the formulae of part (i) into clause form.
(iii) Prove that John likes peanuts using resolution.
(iv) Use resolution to answer the question, "What food does Sue eat?"

Define prior probability and conditional probability. State Bayes's theorem. How is it useful for decision making under uncertainty.