Gujarat technological University
Information Technology
Semester : 8
Subject code : 180703
Subject name : Artificial Intelligence
Examination : Summer 2014
Total Marks : 70 Mark
Time: 10:30 pm - 01:00 pm
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Showing posts with label ARTIFICIAL INTELLIGENCE. Show all posts
Showing posts with label ARTIFICIAL INTELLIGENCE. Show all posts
Artificial Intelligence Summer 2014 Question paper
CS2351 Artificial Intelligence Important Questions for May June 2013 Exam
Anna University
Department of Computer Science Engineering
Semester : 6
Department : CSE
Subject Code : CS2351
Subject Name : Artificial Intelligence
Unit 1
1) Explain in detail about Agents and their types.
2) Explain in detail about uninformed search strategies with example.
3) Explain in detail about informed search strategies with example.
4) Explain in detail about Constraint satisfaction problem with example.
Unit 2
1. Explain in detail about forward & backward chaining algorithm with example.
2. Explain in detail about First order Logic & Inferences in First Order Logic with example.
3. Explain in detail about logical agents with example.
4. Explain in detail about Resolution & Resolution inference Rule with example.
5. Explain with at least 4 examples for PEAS cycle.
Unit 3
1. Explain about partial order planning with an example.
2. Explain about the different types of state space searches.
3. Explain about partial order planning algorithm.
4. Describe in detail about planning graphs.
5. Explain in detail about graph plan algorithm.
6. Explain in detail about conditional planning with an example.
7. Explain about replanning agent algorithm.
Unit 4
1. Explain in detail about Bayesian networks with an example.
2. Explain in detail about conditional probability.
3. Explain in detail about Markov Process with example.
4. Explain in detail about dynamic Bayesian networks.
5. Explain in detail about hidden markov models with example.
6. Explain in detail about inference in Bayesian network.
Unit 5
1. Explain the learning decision tree with algorithm with example.
2. (i).Explain the explanation based learning?
(ii).Explain how learning with complete data is achieved?
3. Discuss learning with hidden variables?
4. Explain all the statistical learning method with example.
5. Explain in detail about Reinforcement learning.
ARTIFICIAL INTELLIGENCE–Nov / Dec 2011 Question Paper
B.E./B.Tech. DEGREE EXAMINATION, NOVEMBER/DECEMBER 2011.
Sixth Semester
Computer Science and Engineering
CS 2351 — ARTIFICIAL INTELLIGENCE
(Common to Seventh Semester – Electronics and Instrumentation Engineering)
(Regulation 2008)
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.
Or
(b) Explain the constraint satisfaction procedure to solve the cryptarithmetic problem.
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 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. | ||
| 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) Probafilistic Reasoning.
15. (a) Explain in detail learning from observation and explanation based learning.
Or
(b) Explain in detail statistical learning methods and reinforcement learning.
———————
ARTIFICIAL INTELLIGENCE–April / May 2011 Question Paper
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 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)
ARTIFICIAL INTELLIGENCE–Question Bank (All Units)
Anna University
ARTIFICIAL INTELLIGENCE
Question Bank
SUBJECT NAME : ARTIFICIAL INTELLIGENCE
SUBJECT CODE : CS2351
YEAR/SEM :III / VI
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 search strategies 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 of Greedy 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)
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