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B.E/B.TECHDEGREEEXAMINATION,MAY/JUNE2010
Sixthsemester (Regulation2008)
Computerscienceandengineering
CS2351–ARTIFICIALINTELLIGENCE
Time: three hours
maximum: 100 marks
Answer ALL questions
PART A- (10 x 2 = 20 marks)
1. Define ideal rational agent.
2. How will you measure the problem-solving performance?
3. State the reasons when the hill climbing often gets stuck.
4. What is constraint Satisfaction problem?
5. Differentiate between prepositional versus first-order logic.
6. Define ontological engineering.
7. What is explanation-based engineering?
8. State the advantages of inductive logic programming.
9. Give the component steps of communication.
10.What are machine translation system?
PART B – (5 x 16 = 80 marks)
11 (a) Explain the structure of agents with suitable diagram. [Marks 16]
or
(b) Explain the following uninformed search strategies.
(i) Iterative deepening depth-first search.[Marks 8]
(ii)Bidirectional search. [Marks 8]
12 (a) Explain the A* search and give the proof of optimality of A*. [Marks 16].
or
(b) Describe Min-Max Algorithm and Alpha-Beta pruning. [Marks 16]
13 (a) (i) Describe the general process of knowledge engineering. [Marks 8]
(ii)Discuss the syntax and semantics of first-order-logic. [Marks 8]
or
(b) Describe the forward chaining and backward chaining algorithm with suitable example.
[Marks 16]
14 (a) (i) Describe the decision tree learning algorithm. [Marks 8]
(ii)Explain the relevance-based learning. [Marks 8]
or
(b) Discuss active and passive reinforcement learning with suitable examples. [Marks 16]
15 (a) (i) Describe the semantic interpretation. [Marks 8]
(ii)Illustrate the grammar induction with suitable example. [Marks 8]
or
(b) Discuss on information retrieval systems and information extraction systems. [Marks
8+8]
B.E/B.TECH DEGREE EXAMINATION, MAY/JUNE 2009
Sixth semester 3rd year.(Regulation 2008)
Department of Computer science and engineering
CS 2351 – ARTIFICIAL INTELLIGENCE
(Common to B.E (part –time) fifth semester regulation 2005)
PART A- (10 x 2 = 20 marks)
1. Define ideal rational agent
2. Define a data type to represent problems and nodes.
3. How does one characterize the quality of heuristic?
4. Formally define game as a kind of search problems.
5. Joe, tom and Sam are brothers-represent using first order logic symbols.
6. List the canonical forms of resolution.
7. What is Q-learning?
8. List the issues that affect the design of a learning element.
9. Give the semantic representation of “john loves Mary”.
10. Define DCG.
PART B – (5 x 16 = 80 marks)
11. (a) explain uninformed search strategies.(16)
(Or)
(b) How searching is used to provide solutions and also describe some real world problems? (16)
12. (a) describe alpha-beta pruning and its effectiveness.(16)
(Or)
(b) Write in detail about any two informed search strategies. (16)
13. (a) elaborate forward and backward chaining.(16)
( Or)
(b) Discuss the general purpose ontology with the following elements:
(i) Categories (4)
(ii) Measures (4)
(iii) Composite objects (4)
(iv) Mental events and mental objects.(4)
14. (a) explain with an example learning in decision trees.(16)
(Or)
(b) Describe multilayer feed-forward networks. (16)
15.(a) (i) list the component steps of communication.(8)
(ii) Write short notes about ambiguity and disambiguation.(8)
(Or)
(b) Discuss in detail the syntactic analysis (PARSING). (16)
Anna University
B.E./B.Tech. DEGREEEXAMINATION, NOVEMBER/DECEMBER 2011.
SixthSemester
Computer Scienceand Engineering
CS 2351 — ARTIFICIAL INTELLIGENCE
(Common to SeventhSemester – Electronicsand Instrumentation Engineering)
(Regulation 2008)
Time : Three hours
Maximum : 100 marks
Answer ALL questions.
PART A — (10 × 2 = 20marks)
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 statespacesearch and planspacesearch.
6. Define partial order planning.
7. List two applications of Hidden Markov model.
8. What are the logics used in reasoning with uncertaininformation?
9. Define Inductive learning.
10. Distinguish between supervised learning and unsupervised learning.
PART B — (5 × 16 = 80marks)
11. (a) Explain AO* algorithm with a suitable example. State the limitations in
the algorithm.
Or
(b) Explain the constraint satisfaction procedure to solve the crypt arithmetic problem.
CROSS
+ ROADS
-----------DANGER
12. (a) Consider the following facts
Team India
Team Australia
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".
Or
(b) 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 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
13. (a) Explain the concept of planning with state space search. How is it different
from partial order planning?
OR
(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.
Or
(b) Write short notes on : (i) Temporal
models (ii) Probabilistic Reasoning.
15. (a) Explain in detail learning from observation and explanation based
learning.
Or
(b) Explain in detail statistical learning methods and reinforcement learning.
Anna University
B.E./B.Tech. DEGREE EXAMINATION, APRIL/MAY 2011
Sixth Semester
Computer Science and Engineering
CS 2351 — ARTIFICIAL INTELLIGENCE (Regulation 2008)
Time : Three hours
Maximum : 100 marks
Answer ALL questions
PART A — (10 × 2 = 20 marks)
1. List down the characteristics of intelligent agent.
2. What do you mean by local maxima with respect to search technique?
3. What factors determine the selection of forward or backward reasoning
approach for an AI problem?
4. What are the limitations in using propositional logic to represent the
knowledge base?
5. Define partial order planner.
6. What are the differences and similarities between problem solving and
planning?
7. List down two applications of temporal probabilistic models.
8. Define Dumpster-Shafer theory.
9. Explain the concept of learning from example.
10. How statistical learning method differs from reinforcement learning method?
PART B — (5 × 16 = 80 marks)
11. (a) Explain in detail on the characteristics and applications of learning agents.
Or
(b) Explain AO* algorithm with an example.
12. (a) Explain unification algorithm used for reasoning under predicate
logic with an example.
Or
(b) Describe in detail the steps involved in the knowledge Engineering process
13. (a) Explain the concept of planning with state space search using
suitable examples. Or
(b) Explain the use of planning graphs in providing better heuristic
estimates with suitable examples.
14. (a) Explain the method of handling approximate inference in
Bayesian Networks. Or
(b) Explain the use of Hidden Markov Models in Speech Recognition.
15. (a) Explain the concept of learning using decision trees and
neural network approach.
Or
(b)Writeshort
notes on : (i)
Statistical
learning. (8)
(ii) Explanation based learning. (8)