AI-Question bank and Lecture Notes

University Question Paper April/MAY 2011

2012-04-13T22:34:00.001-07:00

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 Dempster-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) Write short notes on :
(i) Statistical learning. (8)
(ii) Explanation based learning. (8)

Important 16 mark questions.

2012-03-29T22:33:00.000-07:00

UNIT I - V

1. Explain the following uninformed search strategies.

(i) Iterative deepening depth-first search.
(ii) Bidirectional search.

(iii) Depth limited search.

(iv) Breadth first search.

2. Discuss any two uninformed search methods with examples.

3. Explain the following search strategies.

(i) Min-Max search procedure.

(ii) A*(AO*) search.

(iii) Alpha beta Pruning Algorithm.

4. (i) Explain how a constraint satisfaction problem (CSP) may be solved.

(ii) Describe any one algorithm for determining optimal moves in an adversarial search (games).

5. i) Describe the general process of knowledge engineering.

(ii) Discuss the syntax and semantics of first-order logic.

6. Explain the resolution procedure in detail.

7. Explain the Unification used for reasoning under predicate logic with Eg.

8. Describe the forward chaining and backward chaining algorithm with suitable example.

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

10. Explain the use of planning graphs in providing better heuristic estimates with suitable Eg.

11. (i) Explain the Hidden Markov Models.

(ii)Explain about inference in temporal Models.

12. Explain the method of handling approximate inference in Bayesian networks.

13.Give a brief overview on decision tree inductive learning algorithm.

14.Discuss the following:

(i) Passive reinforcement learning.

(ii) Active reinforcement learning.

15.(i) Outline the different forms of learning.

(ii) Explain the main points of explanation based learning (EBL).Indicate how prior knowledge is used in this technique.

16. Explain the concept of learning using decision tree & Neural network approach.

Unit 5 - 16 mark quest

2011-03-21T21:38:00.001-07:00

Unit 5

16 mark Questions

1. Explain in detail about Learning with Decision Tree?

2. Write about Explanation Based Learning?

3. Explain about Statistical Learning?

4. Explain in detail about Reinforcement Learning?

5. a) Give a brief overview on decision tree inductive learning algorithm. (16)

6. Discuss the following:

(i) Passive reinforcement learning. (8)

(ii) Active reinforcement learning. (8)

Unit 5 - 2 mark quest

2011-03-14T02:40:00.000-07:00

Unit 5

2 mark questions.

1. Define Learning Agent.
2. List out the 3 types of Machine Learning.
3. Define Supervised Learning.
4. Define UnSupervised Learning.
5. Define reinforcement Learning.
6. Define Decision tree
7. Define training Set
8. What is information gain.
9. Define test set.
10. What is Overfitting.
11. What is the solution for overfitting.
12. What memoization.
13. Define Bayesian learning.
14. Define MAP.
15.Define MDL.
16. What is Maximum likelihood
17.What is reward.
18.Define MDP.
19. Define Passive Learning.
20. Define Active Learning.

Unit 4 -16 mark questions.

2011-03-14T01:59:00.000-07:00

Unit 4

16 mark questions

1. Write in detail of basic probability.
2. Explain in detail about Bayesian Network.
3. Explain about inference in Bayesian Network & their types.
4.What is Temporal Model write in detail.
5. Explain about Hidden Markov Model.

Unit 4 - 2 mark quest

2011-03-14T01:56:00.000-07:00

Unit 4

2 Mark Questions

1. Define Uncertainty
2. List out the 3 reasons why medical diagnosis fails to use FOL
3. Define Conditional Probability & its other name.
4. Define UnConditional Probability & its other name.
5. Define Decision theory
6. What are the 3 types of random variables
7. State the Product rule.
8. What is Kolmogorov's axioms.
9. Give an example for bayesian network
10. What is CPT.
11. Define Markov Assumption.
12. Define transition Model.
13. Define Sensor Model.
14. What are the two types of Markov Process.
15. What are the basic inference tasks.
16. Define Hidden Markov Model.

Unit 3 - 16 mark quest

2011-02-28T21:15:00.000-08:00

16 Mark Questions

Part B

   1. Explain in detail about
       (a) Forward State Space Search (8)
     (b) Backward State Space Search (8)
   2. Explain about Partial Order Planning with example (16)
   3. (i) Write about Planning Graph (8)
   (ii) Explain the job scheduling problem with resource constraint (8)
4. Write in detail Conditional Planning with example. (16)
5. Explain about Execution Monitoring & Re planning with example(16)
6. Explain in detail the continuous planning with example.(16)

Unit 3 - 2 mark quest

2011-02-28T21:05:00.002-08:00

2 mark questions

Part- A

What is Ontological Engineering
Define Classical Planning
Define Action.
What is precondition.
What is Effects.
Abbreviate ADL & PDDL .Ans:- Action description language,Planning Domain Definition Language (PDDL)
Define Progression Planning.
What is backward state space search.
Define relaxed problem.
Define Sub Goal independence.
What is Partial Order Planning.
Define Mutex.
What are the 3 levels of heuristics.
Define CPM
Define Slack
Define External precondition & Types of Effects.
Define Indeterminacy & their types.
What are the planning methods of indeterminacy.
Define Execution Monitoring.
Define Continuous Planning.
What are the 2 types of Multi-Agents
Define Sensorless Planning.

Unit 2-16 mark

2011-01-28T23:37:00.000-08:00

1) (i) Write short notes on syntax & semantics of propositional logic (8)
(ii) Explain Conjunctive normal form with example (8)

2) (i) Define the syntactic elements of first-Order logic (8)
(ii) Illustrate the use of first-order logic to represent knowledge. (8)

3) Explain the steps involved in the knowledge Engineering process. Give an example. (16)

4) Explain with an example

(a) forward chaining (8)

(b) Backward chaining (8)
5) (i) Explain the unification with an example.(8)
(ii) Explain Resolution for first order logic with example. (8)

Unit 2-2 mark

2011-01-24T20:17:00.000-08:00

Unit II

2 Mark questions

What is propositional logic?
What are the elements of propositional logic?
What is inference?
Define Modus Ponen’s rule in Propositional logic
What is entailment?
What are knowledge based agents?
What is Knowledge Base?
Explain in detail the connectives used in propositional logic.
Define First order Logic?
Specify the syntax of First-order logic in BNF form.
What are the syntactic elements of First Order Logic?
What the types of quantifiers?
Explain Universal Quantifiers with an example
Explain Existential quantifiers with an example.
What are nested quantifiers?
Explain the connection between Universal Quantifiers and Existential quantifiers
What are the steps associated with the knowledge Engineering process?
Give examples on usage of First Order Logic.
What is universal instantiation?
What is forward chaining? Explain with an example.
What is backward chaining ? Explain with an example.
What are semantic networks?
Define generalized Modus Ponen’s rule in First order Logic.
Define a Sentence?
What are the components of Propositional Logic(syntax & Semantics)?
What is Horn Clause?
Differentiate between prepositional versus first-order logic
What is unification algorithm?
How can you represent the resolution in predicate logic?

artificial class notes.

2011-01-12T01:30:00.000-08:00

ai class notes

To see the class notes for Unit I and Unit II click on the above link "ai class notes".

Unit 1

2011-01-03T20:23:00.000-08:00

PART B

1) a. Elaborate the approaches for AI with eg. (8)
    b. How is a task environment specified? (8)
2) What are the task environment natures? (16)
3) a. Describe the various properties of the task environment. (8)
    b. Write PEAS description for at least four agent types. (8)
4) a. Write the environment characteristics of any four agent type. (8)
    b. Explain in detail Simple reflex agent. (8)
5) Explain in detail any of the four agent structure. (16)
6) a. Explain in detail Model based reflex agent. (8)
     b. Explain in detail Goal based reflex agent. (8)
7) a. Explain in detail Utility based reflex agent. (8)
     b. Explain in detail learning agent. (8)
8) Explain in detail Problem solving agent. (16)
9) a. Distinguish an agent of AI and non AI program. (8)
    b. Explain tree search algorithm in detail. (8)
10) Give an example and explain the toy and real world problem. (16)
11) Explain how solutions are searched by a problem solving agent. (16)
12) a. Write short notes on the following Depth First Search, breadth first search,uniform cost search,
       backtracking search. (8)
      b. Write short notes on Iterative deepening depth first search. (8)
13) a. Write short notes on Depth limited search. (8)
      b. State how repeated states are avoided and give an algorithm. (8)
14) Explain any two heuristic searches in detail. (16)
15) a. Explain Hill climbing in detail. (8)
       b. Explain A* search in detail. (8)
16) a. Explain simulated annealing search in detail. (8)
      b. Explain Memory bounded heuristic search in detail. (8)
17) Explain any two local search algorithms in detail. (16)
18) a. Explain genetic algorithm as a local search. (8)
      b. Explain online search agent working using depth first exploration. (8)
19) a. Write in detail the learning of an agent in online search method. (8)
      b. Explain constraint satisfaction problem with an example. (8)

Unit: 1

2011-01-03T20:20:00.000-08:00

2 mark

1) What are the approaches followed to have AI?
2) Define AI.
3) Define Agent with a diagram.
4) What is a rational agent?
5) What are the elements of an agent?
6) State the factors that make up rationality.
7) Distinguish omniscience and rationality.
8) What is a task environment?
9) What is a PEAS description?
10) Write a PEAS description for an automated taxi?
11) Write a PEAS description for a vacuum cleaner?
12) Write a PEAS description for a wumpus world?
13) What is agent program and agent architecture?
14) What is a software agent?
15) State the difference between utility function and performance measure?
16) State the difference between agent function and agent program?
17) Give the steps adopted by a problem solving agent.
18) What is a fringe?
19) How is problem solving algorithm performance measured?
20) What are the components that a node represents in a search tree?
21) What is informed search?
22) What is local search?
23) What are the various types of informed search?
24) When A* is optimal?
25) What is admissible heuristic?
26) What is greedy best first search?
27) What is A* search?
28) What is SMA* search?
29) What are the types of memory bounded heuristic search?
30) What are the factors that affect the quality of a heuristic?
31) What is a local search algorithm?
32) What are the various local search algorithm?
33) What are the problems faced by a local search algorithm?
34) What are the components of a genetic algorithm?
35) What is online search and offline search?