2011 Set2

1. a) Give examples of agent types and their PEAS descriptions.
b) Both the performance measure and utility function measure how well an agent
doing. Explain the difference between the two. [8+8]
2. a) Explain the idea behind bidirectional search.
b) Discuss optimal strategies in a normal search problem. Also explain a partial search
tree for tic-tac- toe game. [8+8]
3. a) Explain in detail about local search for constraint satisfaction problem.
b) Delineate about local search algorithms. [8+8]
4. a) Search in game playing programs always proceed forward from current state to
goal state. Why? Explain.
b) Define cutoff test. Discuss in detail about cutting off search with an example. [8+8]
5. a) Compare forward Vs backward reasoning. Give simple knowledge base of Horn
clauses and also its corresponding AND-OR graph representations.
b) Given the following, Can you prove that the unicorn is mythical? How about
magical? Horned?
If the unicorn is mythical, then it is immortal mammal. If the unicorn is either
immortal or a mammal then it is horned. The unicorn is magical if it is horned. [8+8]
6. a) Write axioms describing the predicates GrandChild, GreatGrandparent, Brother,
Sister, Daughter, Son, Aunt, Uncle, BrotherInLaw, SisterInLaw and FirstCousin.
Find out proper definition of mth cousin n times removed, and write the definition
in first order logic.
b) Discuss skolemization process. [8+8]
7. a) Give a detailed note on partial order planning graphs.
b) Discuss in detail about backward state space search. [8+8]
8. a) Discuss different statistical learning methods in detail.
b) Explain about learning with decision tree. [8+8]