JERUSALME COLLEGE OF ENGINEERINGDEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING

QUESTION BANK

CS2351 ARTIFICIAL INTELLIGENCE

Date of Issue: 18th DecemberPrepared by: R.I.Minu Date of Submision: 30th Jan 2011

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 DecemberDate of Submission: 30th Jan 2011

UNIT IILOGICAL REASONING

PART A1. 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 logic15. What do you understand by logical reasoning16. State the reasons when the hill climbing often gets stuck17. Define unification18. Define resolution19. 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 DecemberDate of Submision: 30th Jan 2011

UNIT IIIPLANNING

PART A1. Define partial order planner (June 07)2. Define planning with state space search3. What is a planning graph4. What is planning and acting in real world5. Define forward state space search6. Define backward state space search7. Heuristics for state space search8. Describe the differences and similarities between problem solving and planning9. What is a planning graph10. What is sub goal independence assumption11. What is empty – delete – list heuristic12. What is least commitment strategy13. What is regression planning14. What is the main advantage of backward search 15. what is progression planning

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16. What is closed world assumption17. What is least commitment18. What is GraphPlan algorithm19. What is Critical Path Method (CPM)20. What is a slack

PART B (8 Mark)

1. Explain Planning with state space search with an example2. Explain partial order planning with example3. Explain Graph Plan algorithm with the example4. What is STRIPS explain in detail with the example5. How we plan and act in non deterministic domains6. What is conditional planning7. How we schedule with resource constraints8. How we plan with propositional logic9. Explain partial order planning with unbound variables10. Give an example for partial order planning11. what is Backward state space search12. Explain Heuristics for state space search13. Explain Forward state space search14. For Air cargo transport explain STRIPS15. For Blocks World explain STRIPS16. Compare STRIPS and ADL language

Date of Issue: 23ed DecemberDate of Submission: 30th Jan 2011

UNIT IVUNCERTAIN KNOWLEDGE AND REASONING

PART A1. 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 Theory5. Define probabilistic inference6. what is Markov blanket7. What is noisy logical relationship8. what is a Temporal Model9. Define HMM10. What is smoothing11. What is hindsight12. Define EM algorithm

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13. define simplified matrix algorithm14. how to handle uncertain knowledge15. what are the basic probability notation16. what is prior probability17. Distinguish between full joint probability distribution and joint probability distribution18. write an algorithm for decision theoretic agent19. what are the axioms of probability20. 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 HMM5. What is a Bayesian network6. How to get the exact inference form Bayesian network7. How to get the approximate inference form Bayesian network8. What are all the temporal model9. How to determine uncertain acting under uncertainty 10. In temporal model explain filtering and prediction11. Explain Smoothing with needed algorithm12. How to handle uncertainty13. How to construct Bayesian network14. What are all the exact inference in Dynamic Bayesian Network15. What are all the approximate inference in DBN16. How to represent knowledge in an uncertain domain

Date of Issue: 23ed DecemberDate of Submission: 30th Jan 2011

UNIT V -- LEARNING

PART A1. 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 learning19. What are the statistical learning method20. 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 algorithm9. 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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