B.E./B.Tech. DEGREE EXAMINATION, NOVEMBER/DECEMBER 2011.
Computer Science and Engineering
CS 2351 — ARTIFICIAL INTELLIGENCE
(Common to Seventh Semester – Electronics and Instrumentation Engineering)
Time : Three hours
Maximum : 100 marks
Answer ALL questions.
PART A — (10 × 2 = 20 marks)
1. What is a rational agent?
2. State the significance of using heuristic functions?
3. Distinguish between predicate and propositional logic.
4. What factors justify whether the reasoning is to be done in forward or backward reasoning?
5. Distinguish between state space search and plan space search.
6. Define partial order planning.
7. List two applications of Hidden Markov model.
8. What are the logics used in reasoning with uncertain information?
9. Define Inductive learning.
10. Distinguish between supervised learning and unsupervised learning.
PART B — (5 × 16 = 80 marks)
11. (a) Explain AO* algorithm with a suitable example. State the limitations in the algorithm.
(b) Explain the constraint satisfaction procedure to solve the cryptarithmetic problem.
12. (a) Consider the following facts
Final match between India and Australia
India scored 350 runs Australia score 350 runs India lost 5 wickets Australia lost 7 wickets
The team which scored the maximum runs wins
If the scores are same then the team which lost minimum wickets wins the match.
Represent the facts in predicate, convert to clause form and prove by resolution
"India wins the match".
Analyse the missionaries and Cannibals problem which is stated as follows. 3 missionaries and 3 cannibals are on one side of the river
along with a boat that can hold one or two people. Find a way to get everyone to the other side, without leaving a group of missionaries in one place out numbered by the cannibals in that place.
(i) Formulate a problem precisely making only those distinctions necessary to ensure a valid solution. Draw a diagram of the complete state space.
(ii) Design appropriate search algorithm for it.
Explain the concept of planning with state space search. How is it different from partial order planning?
(b) What are planning graphs? Explain the methods of planning and acting in the real world.
14. (a) Explain the concept of Bayesian network in representing knowledge in an uncertain domain.
(b) Write short notes on : (i) Temporal models
(ii) Probafilistic Reasoning.
15. (a) Explain in detail learning from observation and explanation based learning.
(b) Explain in detail statistical learning methods and reinforcement learning.