Anna University May/June 2012 examination

Important Questions

CS2351 ARTIFICIAL INTELLIGENCE

Unit I

**1.**Explain A* algorithm with a suitable example. State the limitations in the algorithm.

**2.**Explain in detail on the characteristics and applications of learning agents.

**3.**What are the four basic types of agent program in an intelligent system? Explain how did you convert them into learning agents?

**4.**Define CSP and Discuss about backtracking search for CSPs

Unit II

1.Discuss forward and backward chaining in detail

2.Discuss in detail about FOL and inferences in FOL.

3.Explain Unification algorithm used for reasoning under predicate logic with an example

4.Analyze 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 the way to get everyone to the other side, without leaving a group of missionaries in one place outnumbered 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.

5.Explain in detail about Resolution with its application.

Unit III

1.Explain the concept of planning with state space search using suitable examples.

2.Explain the use of planning graphs in providing better heuristic estimates with examples

3.Write short notes on the following

i.Conditional planning

ii.Execution monitoring and replanning

iii.Continuous planning

iv.Multiagent planning

4.Explain any two real world applications of planning and acting

Unit IV

1.Explain the method of handling Approximate inference in Bayesian networks.

2.Explain the use of Hidden Markov Models in Speech Recognition in detail

3.Explain in detail about (1)Temporal models(2)Probabilistic reasoning

4.Explain the concept of Bayesian network in representing knowledge in an uncertain domain

UNIT V

1.Explain the concept of learning using decision trees and neural network approach.

2.Explain in detail learning from observation and explanation based learning

3.Explain in detail statistical learning methods and reinforcement learning.

4.Write short notes on (a) Statistical learning (b) Explanation based learning