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)
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