QUESTION BANK- CS2351 ARTIFICIAL INTELLIGENCE

QUESTION BANK

CS2351 ARTIFICIAL INTELLIGENCE

UNIT I

PROBLEM SOLVING

2 MARK QUESTIONS

1) What are the approaches followed to have AI?

2) Define AI.

3) Define Agent with a diagram.

4) What is a Ideal 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)What is agent program and agent architecture?

13)What is a software agent?

14) State the difference between utility function and performance measure?

15) State the difference between agent function and agent program?

16)Give the steps adopted by a problem solving agent.

17)What is a fringe?

18)How is problem solving algorithm performance measured?

19)What are the components that a node represents in a search tree?

20)What are the different approaches in defining artificial intelligence?

21)Define an agent.

22)What is bounded rationality?

23)What is an autonomous agent?

24)Describe the salient features of an agent.

25)Define the terms: agent, agent function

8 MARK QUESTION

1) How is a task environment specified?

2) What are the task environment natures?

3) Describe the various properties of the task environment.

4) Write PEAS description for at least four agent types. (UNIVERSITY QUESTION)

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5) Write the environment characteristics of any four agent type.

6) Explain in detail Simple reflex agent.

7) Explain in detail any of the four agent structure.

8) Explain in detail Model based reflex agent.

9) Explain in detail Goal based reflex agent.

10) Explain in detail Utility based reflex agent.

11) Explain in detail learning agent.

12)Distinguish an agent of AI and non AI program.

13) Explain tree search algorithm in detail.

14) Write short notes on Iterative deepening depth first search. (UNIVERSITY QUESTION)

15) Write short notes on Depth limited search. (UNIVERSITY QUESTION)

16) State how repeated states are avoided and give an algorithm.

17) Explain Depth-First search (UNIVERSITY QUESTION)

18) Explain Iterative deepening depth first search (UNIVERSITY QUESTION)

19) Explain Bidirectional search (UNIVERSITY QUESTION)

20) Explain the PEAS specification of the task environment of an agent (UNIVERSITY

QUESTION)

Date of Issue: 23ed December

Date of Submission: 30th Jan 2011

UNIT II

LOGICAL REASONING

PART A

1. What are the two commitments of logic and define them?

2. What are the components of a first order logic?

3. What is the difference between the two quantifiers in the logics?

4. What is synchronic and diachronic?

5. What are casual rules?

6. What are diagnostic rules?

7. What is a model based reasoning systems?

8. What are the various steps in knowledge engineering process of a first order logic?

9. What are the various resolution strategies?

10. What is ontological engineering?

11. What is upper ontology?

12. What distinguish general purpose ontology and special purpose ontology?

13. What are categories and objects?

14. Describe default logic

15. What do you understand by logical reasoning

16. State the reasons when the hill climbing often gets stuck

17. Define unification

18. Define resolution

19. What is reification?

20. List the canonical forms of resolution

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PART B (8 MARK QUESTION)

1. Give the Syntax and Semantics of a first order logic in detail with an eg.

2. Give Syntax and Semantics of a first order logic for a family domain.

3. Give the Syntax and Semantics of a first order logic for Numbers, Sets, Lists domain.

4. Elaborate upon the process of knowledge engineering with electronic circuit’s domain.

5. Explain about unification with an algorithm in a first order logic.

6. Explain in detail the concept of theorem proverbs.

7. Explain forward chaining and backward chaining in detail for a first order definite

clauses. (UNI QUES)

8. Explain how categories and objects are presented in any four sets.

9. Elaborate upon the ontology for situation calculus.

10. Elaborate upon the ontology for event calculus.

11. Explain predicate logic (UNI QUES)

12. Write notes on proposition logic (UNI QUES)

13. Explain the resolution procedure with an example (UNI QUES)

14. Illustrate the use of predicate logic to represent the knowledge with suitable example

(UNI QUES)

15. With an example explain the logics for non monotonic reasoning (UNI QUES)

16. How facts are represented using prepositional logic? give an example (UNI QUES)

Date of Issue: 18th December

Date of Submision: 30th Jan 2011

UNIT III

PLANNING

PART A

1. Define partial order planner (June 07)

2. Define planning with state space search

3. What is a planning graph

4. What is planning and acting in real world

5. Define forward state space search

6. Define backward state space search

7. Heuristics for state space search

8. Describe the differences and similarities between problem solving and planning

9. What is a planning graph

10. What is sub goal independence assumption

11. What is empty – delete – list heuristic

12. What is least commitment strategy

13. What is regression planning

14. What is the main advantage of backward search

15. what is progression planning

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16. What is closed world assumption

17. What is least commitment

18. What is GraphPlan algorithm

19. What is Critical Path Method (CPM)

20. What is a slack

PART B (8 Mark)

1. Explain Planning with state space search with an example

2. Explain partial order planning with example

3. Explain Graph Plan algorithm with the example

4. What is STRIPS explain in detail with the example

5. How we plan and act in non deterministic domains

6. What is conditional planning

7. How we schedule with resource constraints

8. How we plan with propositional logic

9. Explain partial order planning with unbound variables

10. Give an example for partial order planning

11. what is Backward state space search

12. Explain Heuristics for state space search

13. Explain Forward state space search

14. For Air cargo transport explain STRIPS

15. For Blocks World explain STRIPS

16. Compare STRIPS and ADL language

Date of Issue: 23ed December

Date of Submission: 30th Jan 2011

UNIT IV

UNCERTAIN KNOWLEDGE AND REASONING

PART A

1. Define uncertainty (june 07)

2. Define Baye’s rule (june 06)

3. How is uncertainty knowledge represented ? Give an example (Dec 05)

4. Define Decision Theory

5. Define probabilistic inference

6. what is Markov blanket

7. What is noisy logical relationship

8. what is a Temporal Model

9. Define HMM

10. What is smoothing

11. What is hindsight

12. Define EM algorithm

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13. define simplified matrix algorithm

14. how to handle uncertain knowledge

15. what are the basic probability notation

16. what is prior probability

17. Distinguish between full joint probability distribution and joint probability distribution

18. write an algorithm for decision theoretic agent

19. what are the axioms of probability

20. what is inference.

PART B (8 Mark)

1. How to deal with uncertainty (dec 05)

2. What is Baye’s rule ? explain how Baye’s rule can be applied to tackle uncertain

knowledge (june 07)

3. Explain probabilistic reasoning ( june 07)

4. Explain HMM

5. What is a Bayesian network

6. How to get the exact inference form Bayesian network

7. How to get the approximate inference form Bayesian network

8. What are all the temporal model

9. How to determine uncertain acting under uncertainty

10. In temporal model explain filtering and prediction

11. Explain Smoothing with needed algorithm

12. How to handle uncertainty

13. How to construct Bayesian network

14. What are all the exact inference in Dynamic Bayesian Network

15. What are all the approximate inference in DBN

16. How to represent knowledge in an uncertain domain

Date of Issue: 23ed December

Date of Submission: 30th Jan 2011

UNIT V -- LEARNING

PART A

1. What are the types of learning?

2. What is ensemble learning?

3. Give a simple mathematical model for a neuron.

4. What are the two choices for activation function?

5. What are the categories of neural network structures?

6. What is memorization?

7. State the factors involved in analysis of efficiency gains from EBL.

8. State the design issues that affect the learning element.

9. State the factors that play a role in the design of learning systems.

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10. State the decision tree as a performance element.

11. What is explanation based learning ( May 2010, june 06)

12. State the advantages of inductive logic programming (May 2010)

13. How to represent experience using learning techniques (Dec 05)

14. What is meant by decision network (June 06)

15. List the issues that affect the design of an learning element (June 09)

16. What is Q learning (June 09)

17. What is meant by proof by refutation (June 2007)

18. Define reinforcement learning

19. What are the statistical learning method

20. Define inductive learning

PART B (8 Marks)

1. Explain the various forms of learning.

2. How is the learning process in a decision tree?

3. Explain the various methods of logical formulation in logical learning?

4. How are explanation based learning done?

5. Elaborate upon inductive logic programming.

6. Write in detail the EM algorithm.

7. Give an overview of a neural network.

8. Explain multilayer feed forward neural networks with an algorithm

9. Explain the nonparametric learning methods.

10. How learning is done on a complete data using statistical methods?

11. Explain the relevance based learning (May 10)

12. Describe the decision tree learning algorithm (May 2010)

13. Discuss active reinforcement leaning (May 10)

14. Discuss passive reinforcement learning (May 10)

15. How to further proceed to decision making (Dec 05)

16. Describe multilayer feed forward network (June 09)

UNIT I PROBLEM SOLVING

PART-A

1. What is Intelligence?

2. Describe the four categories under which AI is classified

with examples.

3. Define Artificial Intelligence.

4. List the fields that form the basis for AI.

5. What is a Knowledge Based System? Explain.

6. List a few of the task domains of AI.

7. Describe the components of a KBS.

8. What id meta-knowledge?

9. Expand LISP and PROLOG.

10. What is a Production System?

11. Define state-space search technique.

12. List the steps in performing a state-space search.

13. What is heuristic search?

14. Differentiate Informed & Uninformed search. Give

examples.

15. Define the logic behind – Hill climbing, Best-First Search,

BFS and DFS.

16. What do you mean by Game Playing?

17. What are the components of a Game software?

18. What is a plausible-move generator? What is its role?

19. Define alpha & beta values in a game tree.

20. Mention some of the

knowledge representation techniques.

PART-B

1) What are the four basic types of agent program in any

intelligent system? Explain how did you convert them into

learning agents?. (16)

2) Explain the following uninformed search strategies with

examples.

(a) Breadth First Search. (4)

(b) Uniform Cost Search (4)

(c) Depth First Search (4)

(d) Depth Limited Search (4)

3) What is PEAS? Explain different agent types with their

PEAS descriptions. (16)

4) Explain in detail the properties of Task Environments. (16)

5) Define a problem and its components. Explain how a

problem solving agent works? (16)

6) Explain real-world problems with examples. (16)

7) Explain in detail with examples

(i) Iterative deepening search (8)

(ii) Bidirectional search (8)

8) How an algorithm’s performance is evaluated? Compare

different uninformed searchstrategies in terms of the four

evaluation criteria. (16)

UNIT II LOGICAL REASONING

PART-A

1. Differentiate prepositional & predicate logic.

2. What is clausal form? How is it useful?

3. Define a well-formed formula (wff).

4. List some of the rules of inference.

5. What is resolution /refutation?

6. Define unification.

7. What are semantic nets?

8. What are frames? How do they differ from semantic nets.

9. What are script? What is its use?

10. List the components of a script.

11. Mention the frame manipulation primitives.

12. Define forward and backward chaining. Differentiate the

same.

13. What is means-end analysis?

14. Mention the strategies used in resolving clauses (unit-

preference, set-of-support, best first)

PART-B

1) What is Greedy Best First Search? Explain with an

example the different stages ofGreedy Best First search.

(16)

2) What is A* search? Explain various stages of A* search

with an example. (16)

3) Explain in detail with examples

(i) Recursive Best First Search(RBFS) (8)

(ii) Heuristic Functions (8)

4) Explain the following local search strategies with

examples.

(i) Hill climbing (4)

(ii) Genetic Algorithms (4)

(iii) Simulated annealing (4)

(iv) Local beam search (4)

5) Define constraint satisfaction problem (CSP). How CSP is

formulated as a search prob- lem? Explain with an example.

(16)

6) Explain with examples

(i) Constraint graph (4) (ii) Cryptarithmetic problem (4) (iii)

Adversarial search problem (4) (iv) Game (4)

7) Explain with algorithm and example :

i. Minimax algorithm (8)

ii. Alpha-Beta Pruning (8)

UNIT – III

PART-A

1. Describe Bayes theorem.

2. What are the disadvantages of Closed World Assumption

(CWA). How will you over- come it?

3. Define Non monotonic reasoning.

4. What are Truth Maintenance Systems? Draw its block

diagram.

5. What are Bayesian networks? Give an example.

6. What is fuzzy logic? What is its use?

7. How Knowledge is represented?

8. What is propositional logic?

9. What are the elements of propositional logic?

10. What is inference?

11. What are modus ponens?

12. What is entailment?

13. What are knowledge based agents?

PART-B

1) (i) Define the syntactic elements of first-Order logic (8)

(ii) Illustrate the use of first-order logic to represent

knowledge. (8)

2) Explain the steps involved in the knowledge Engineering

process. Give an example. (16)

3) Explain with an example

(a) forward chaining (8) (b) Backward chaining (8)

4) Give resolution proof for example problem statement :

(a) “West is a criminal” (8) (b) Curiosity killed the cat (8)

5) What is Ontological Engineering? Explain with the

diagram the upper ontology of the world. (16)

6) How categories are useful in knowledge representation.

(16)

7) What is situation calculus? Explain the ontology of

situation calculus. (16)

8) What is a frame problem? (4) How do you solve the

following problems in situation calculus?

(a) Solving the representational frame problem (6)

(b) Solving the inferential frame problem (6)

9) Write sort notes on

(a) Event calculus (4)

(b) Generalized events (4)

(c) Intervals (4)

(d) Fluents and objects (4)

10) Explain in detail the shopping agent for the Internet

shopping world example. (16)

Unit IV

Part A

1. Define linguistics. List the general classification of languages.

2. Construct parse trees for given sentences.

3.Waht are grammars?

4.Give the syntactic tree for the sentence „The boy ate the

apple.

5. List the types of grammars.

6. What is parsing? What is its importance?

7. Differentiate – Top down & Bottom Up parsing,

Deterministic & Non deterministic parsing,.

8. What are Recursive transition networks (RTN), Augmented

Transition Networks (ATN)?

9. What is the role of semantic analysis in NLP?

10. Define Natural Language generation.

11.List any two NLP systems.

12.What is distributed reasoning?

13. What are Intelligent Agents? What are its use?

PART-B

1) What are the components of agents? (16)

2) Define and explain

(i) Supervised learning (6) (ii) Unsupervised learning

(6) (iii) Reinforcement learning (4)

3) How hypotheses formed by pure inductive inference or

induction?Explain with ex - amples. (16)

4) (a) What is a decision tree? (4)

b) Explain the process of inducing decision trees from

examples. (6)

c) Write the decision tree learning algorithm (6)

5) How the performance of a learning algorithm is assessed?

Draw a learning curve for the decision tree algorithm (16)

6) Explain with an example

(a) Ensemble learning (4)

(b) Cumulative learning process (4)

(c) Relevant based learning(RBL) (4)

(d) Inductive logic programming (4)

7) What is explanation based learning? Explain in detail with

an example. (16)

8) What is Inductive Logic Programming? Write

FOIL algorithm for learning sets of first- order horn clauses

from example. (16)

9) Discuss on learning with hidden variables : the

EM algorithm. (16)

10) What is reinforcement learning? Explain (a) Passive

reinforcement learning (b) Active reinforcement learning

(16)

UNIT – V

PART-A

1. What are Expert Systems?

2. Briefly explain the knowledge acquistion process.

3. List the characteristic features of a expert system.

4. Mention some of the key applications of ES.

5. What is learning? What are its types?

6. Define generalization.

7. Define Inductive Bias.

8. What is Explanation Based Learning? How is it useful?

PART-B

1) Define the terms a) Communications (b) Speech act (c)

Formal Language and (d) Gram- mar (16)

2) What are the component steps in communication? Explain

the steps for the example sentence “The wumpus is dead”

(16)

3) Contruct a lexicon and grammar for a small fragment of

English Language. (16)

4) What is parsing? Explain in detail two parsing methods

and give a trace of a bottom up parse on the string “The

wumpus is dead” (16)

5) What is augmented grammar? Explain with examples

(a) Verb sub categorization (8)

(b) Semantic interpretation (8)

6) Discuss ambiguity and disambiguation. (16)

7) What is Grammar indication? Explain with an example (16)

8) Explain in detail

(a) Information Retrieval (8)

(b) Information Extraction (8)

9) What is machine translation? What are different types

of machine translation? (16)

10) Draw the schematic of a machine translation and explain

for an example problem (16)