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Oct 15, 2014

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Artificial intelligence[ECS-801]

Academic Session 2011-12

Minakshi bhattAssistant professor Deptt. of Computer Sc. & Engg..

Course File/Minakshi bhatt Assistant Professor C.S.E. deptt.

Artificial intelligence[ECS-801]

S.No. 1. 2. 3. 4. 5. 6. Time Table Syllabus

Content

Page No.

Subject Coverage Plan Tutorial Sheets/Assignment Sheets Objective questions Previous Year UPTU Question Papers

Course File/Minakshi bhatt Assistant Professor C.S.E. deptt.

Artificial intelligence[ECS-801]

1st Mon Tue Wed Thu Fri Sat L

2nd

3rd

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8thLab ECS-852 (VIII C)

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LUNCH T(G1+G2)

Course File/Minakshi bhatt Assistant Professor C.S.E. deptt.

Artificial intelligence[ECS-801]

ARTIFICIAL INTELLIGENCE (TIT- 702) UNIT -I

Introduction : Introduction to Artificial Intelligence, Foundations and History of Artificial Intelligence, Applications of Artificial Intelligence, Intelligent Agents, Structure of Intelligent Agents. Computer vision, Natural Language Possessing.UNIT - II

Introduction to Search : Searching for solutions, Uniformed search strategies, Informed search strategies, Local search algorithms and optimistic problems, Adversarial Search, Search for games, Alpha - Beta pruningUNIT III

Knowledge Representation & Reasoning: Propositional logic, Theory of first order logic, Inference in First order logic, Forward & Backward chaining, Resolution, Probabilistic reasoning, Utility theory, Hidden Markov Models (HMM), Bayesian Networks.UNIT - IV

Machine Learning : Supervised and unsupervised learning, Decision trees, Statistical learning models, Learning with complete data - Naive Bayes models, Learning with hidden data - EM algorithm, Reinforcement learning,UNIT - V

Pattern Recognition : Introduction, Design principles of pattern recognition system, Statistical Pattern recognition, Parameter estimation methods - Principle Component Analysis (PCA) and Linear Discriminant Analysis (LDA), Classification Techniques Nearest Neighbor (NN) Rule, Bayes Classifier, Support Vector Machine (SVM), K means clustering.References: 1. Charnick Introduction to A.I., Addision Wesley 2. Rich & Knight, Artificial Intelligence 3. Winston, LISP, Addision Wesley 4. Marcellous, Expert System Programming, PHI 5. Elamie, Artificial Intelligence, Academic Press 6. Lioyed, Foundation of Logic Processing, Springer Verlag

Course File/Minakshi bhatt Assistant Professor C.S.E. deptt.

Artificial intelligence[ECS-801]

Course File/Minakshi bhatt Assistant Professor C.S.E. deptt.

Artificial intelligence[ECS-801]

1. ----------have the general form: given such and such data, find X. A huge variety of types of problem is addressed in AI. i. ii. iii. iv. Learning Solution Problem All of these

2. To reason is to draw inferences appropriate to the situation in hand is called--------------. i. ii. iii. iv. Solution Learning Problem Reasoning

3. The simplest is learning by---------. i. ii. iii. iv. By example By error Trail by error None

4. The logical programming language-----------was conceived by Alain Colmerauer at the university of Marseilles. i. PROLOG

Course File/Minakshi bhatt Assistant Professor C.S.E. deptt.

Artificial intelligence[ECS-801] ii. iii. iv. LISP C++ None of these

5. --------------is usually defined as the science of making computers do things that require intelligence when done by humans. i. ii. iii. iv. Natural language Learning Artificial intelligence Intelligence

6. -------------system that use forward reasoning. i. ii. iii. iv. AI A problem solving Vision Game playing

7. Attributes of a state altered by an operator application is called----------. i. ii. iii. iv. Post condition Pre post-condition Pre condition States

8. A problem space consists of ------------. i. ii. iii. iv. States Operators Both (a) & (b) None

Course File/Minakshi bhatt Assistant Professor C.S.E. deptt.

Artificial intelligence[ECS-801] 9. Search methods are classified in to mainly---------search methods. i. ii. iii. iv. Four types Three types Two types None

10. uninformed search is also called------. i. ii. iii. iv. Blind search Hill climbing Best first search Worst first search

11. This method is basically a --------- as complete solutions must be created before testing. i. ii. iii. iv. Depth first search Hill climbing Best first search Worst case search

12. ------------is a variation on a hill climbing and the idea is to include a general survey of the scene to avoid climbing false foothills. i. ii. iii. iv. Simulated annealing Best first search Worst case search None

13. A higher energy level is given by where k is-----------. i. ii. Distance Boltzmanns constant

Course File/Minakshi bhatt Assistant Professor C.S.E. deptt.

Artificial intelligence[ECS-801] iii. iv. Variation None

14. -------------generating appropriate NL responses to unpredictable inputs. i. ii. iii. iv. Speech synthesis Speech recognition Natural language understanding Natural language generation

15. In S-ab the terminals here are i. ii. iii. iv. S A A and b None

16. ---------is a finite set of terminals,disjoint with V,which make up the actual content of the sentence. i. ii. iii. iv. V Sigma S R

17. The name expert system was derived from the term------. i. ii. iii. iv. Knowledge base expert system Information based expert system Goal based expert system Data based expert system

18. The general purpose problem solver (GPS),a procedure developed by--------. i. ii. Newell Simon

Course File/Minakshi bhatt Assistant Professor C.S.E. deptt.

Artificial intelligence[ECS-801] iii. iv. Newell & Simon None

19. An expert system is set of programs that manipulate---------to solve problems. i. ii. iii. iv. Encoded knowledge Decoded encoded knowledge Decoded knowledge None

20. ------------a declarative representation of the expertise, often in If THEN rules; i. ii. iii. iv. Working storage Knowledge base Inference engine User interface

21. ------------the data which is specific to a problem being solved. i. ii. iii. iv. Working storage Knowledge base Inference engine User interface

22. ------------the code at the core of the system which drives recommendations from the knowledge base and the problem-specific data in working storage. i. ii. iii. iv. Working storage Knowledge base Inference engine User interface

23. ------------the code that control the dialog between the user and the system. i. Working storage

Course File/Minakshi bhatt Assistant Professor C.S.E. deptt.

Artificial intelligence[ECS-801] ii. iii. iv. Knowledge base Inference engine User interface

24. ---------------the individual or individuals who currently expert solving the problems the system is intended to solve. i. ii. iii. iv. Domain expert Knowledge base Inference engine User interface

25. --------------the ability of the system to reason with the rules and the data which are not precisely known. i. ii. iii. iv. Copying with uncertainty Knowledge base Inference engine User interface

26. --------------is an efficient way to solve problems that can be modeled as structured selection problems. i. ii. iii. iv. Knowledge base Inference engine User interface Goal driven reasoning

27. This is a programming language that was designed for easy manipulation of data strings. It was developed in 1959 by John McCarthy and is still commonly used today in artificial intelligence (AI) programming. i. ii. iii. iv. LISP assembly language machine code Ruby

Course File/Minakshi bhatt Assistant Professor C.S.E. deptt.

Artificial intelligence[ECS-801] 28. This is a system of programs and data structures that approximates the operation of the human brain. i. ii. iii. iv. Intelligent Network decision support system neural network genetic programming

29. This is the ability of a computer to use binocular vision to differentiate between objects. The computer uses high-resolution cameras, a large amount of random access memory (RAM), and an artificial intelligence (AI) program to interpret data i. ii. iii. iv. DiffServ model-view-controller machine vision eye-in-hand system

30. This is a program that allows the computer to simulate conversation with a human being. "Eliza" and "Parry" are early examples of programs that can at least temporarily fool a real human being into thinking they are talking to another person. i. ii. iii. iv. Speech Application Program Interface chatterbot speech recognition Amiga

31. This is a program that gathers information or performs some other service on a regular schedule without a human being's immediate presence. i. ii. iii. iv. aggregator agile applet page intelligent agent

Course File/Minakshi bhatt Assistant Professor C.S.E. deptt.

Artificial intelligence[ECS-801] 32. This is a type of computer program that simulates the judgment and behavior of a human or organization that possesses expert knowledge and experience in a particular field. i. ii. iii. iv. expert system cyborg autonomous system cybrarian

Tutorial /Assignment 01

Course File/Minakshi bhatt Assistant Professor C.S.E. deptt.

Artificial intelligence[ECS-801]

Q.1. What is artificial intelligence? Does artificial intelligence aim at human-level intelligence? Q.2. Define the terms: 1. Turing test 2. Natural language Q.3. Isnt a solid definition of intelligence that doesnt depend on relating it to human intelligence? Q.4. What are the applications of artificial intelligence? Q.5. What is the water jug problem? Q.6. What are

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