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B.E/B.TECHDEGREEEXAMINATION,MAY/JUNE2010 Sixthsemester (Regulation2008) Computerscienceandengineering CS2351–ARTIFICIALINTELLIGENCE Time: three hours maximum: 100 marks Answer ALL questions PART A- (10 x 2 = 20 marks) 1. Define ideal rational agent. 2. How will you measure the problem-solving performance? 3. State the reasons when the hill climbing often gets stuck. 4. What is constraint Satisfaction problem? 5. Differentiate between prepositional versus first-order logic. 6. Define ontological engineering. 7. What is explanation-based engineering? 8. State the advantages of inductive logic programming. 9. Give the component steps of communication. 10.What are machine translation system? PART B – (5 x 16 = 80 marks) 11 (a) Explain the structure of agents with suitable diagram. [Marks 16] or (b) Explain the following uninformed search strategies. (i) Iterative deepening depth-first search.[Marks 8] (ii)Bidirectional search. [Marks 8] 12 (a) Explain the A* search and give the proof of optimality of A*. [Marks 16]. or (b) Describe Min-Max Algorithm and Alpha-Beta pruning. [Marks 16] 13 (a) (i) Describe the general process of knowledge engineering. [Marks 8] (ii)Discuss the syntax and semantics of first-order-logic. [Marks 8] or (b) Describe the forward chaining and backward chaining algorithm with suitable example. [Marks 16] 14 (a) (i) Describe the decision tree learning algorithm. [Marks 8] (ii)Explain the relevance-based learning. [Marks 8] or (b) Discuss active and passive reinforcement learning with suitable examples. [Marks 16] 15 (a) (i) Describe the semantic interpretation. [Marks 8] (ii)Illustrate the grammar induction with suitable example. [Marks 8] or (b) Discuss on information retrieval systems and information extraction systems. [Marks 8+8] B.E/B.TECH DEGREE EXAMINATION, MAY/JUNE 2009 Sixth semester 3rd year.(Regulation 2008) Department of Computer science and engineering CS 2351 – ARTIFICIAL INTELLIGENCE (Common to B.E (part –time) fifth semester regulation 2005) PART A- (10 x 2 = 20 marks) 1. Define ideal rational agent 2. Define a data type to represent problems and nodes. 3. How does one characterize the quality of heuristic? 4. Formally define game as a kind of search problems. 5. Joe, tom and Sam are brothers-represent using first order logic symbols. 6. List the canonical forms of resolution. 7. What is Q-learning? 8. List the issues that affect the design of a learning element. 9. Give the semantic representation of “john loves Mary”. 10. Define DCG. PART B – (5 x 16 = 80 marks) 11. (a) explain uninformed search strategies.(16) (Or) (b) How searching is used to provide solutions and also describe some real world problems? (16) 12. (a) describe alpha-beta pruning and its effectiveness.(16) (Or) (b) Write in detail about any two informed search strategies. (16) 13. (a) elaborate forward and backward chaining.(16) ( Or) (b) Discuss the general purpose ontology with the following elements: (i) Categories (4) (ii) Measures (4) (iii) Composite objects (4) (iv) Mental events and mental objects.(4) 14. (a) explain with an example learning in decision trees.(16) (Or) (b) Describe multilayer feed-forward networks. (16) 15.(a) (i) list the component steps of communication.(8) (ii) Write short notes about ambiguity and disambiguation.(8) (Or) (b) Discuss in detail the syntactic analysis (PARSING). (16) Anna University B.E./B.Tech. DEGREEEXAMINATION, NOVEMBER/DECEMBER 2011. SixthSemester Computer Scienceand Engineering CS 2351 — ARTIFICIAL INTELLIGENCE (Common to SeventhSemester – Electronicsand Instrumentation Engineering) (Regulation 2008) Time : Three hours Maximum : 100 marks Answer ALL questions. PART A — (10 × 2 = 20marks) 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 statespacesearch and planspacesearch. 6. Define partial order planning. 7. List two applications of Hidden Markov model. 8. What are the logics used in reasoning with uncertaininformation? 9. Define Inductive learning. 10. Distinguish between supervised learning and unsupervised learning. PART B — (5 × 16 = 80marks) 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 crypt arithmetic problem. CROSS + ROADS -----------DANGER 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 outnumbered 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) Probabilistic Reasoning. 15. (a) Explain in detail learning from observation and explanation based learning. Or (b) Explain in detail statistical learning methods and reinforcement learning. 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 Dumpster-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)Writeshort notes on : (i) Statistical learning. (8) (ii) Explanation based learning. (8)