SRM UNIVERSITY FACULTY OF ENGINEERING AND TECHNOLOGY SCHOOL - Course Plan for Artificial Intelligence

vinegarclothΤεχνίτη Νοημοσύνη και Ρομποτική

17 Ιουλ 2012 (πριν από 5 χρόνια και 7 μέρες)

401 εμφανίσεις

SRM UNIVERSITY

FACULTY OF ENGINEERING AND TECHNOLOGY


SCHOOL OF COMPUTING

DEPARTMENT OF CSE

COURSE PLAN

Course Code

:

CS0541

Course Title

:

Artificial Intelligence and Intelligence systems

Semester


: I

Course Time

:

J
uly



Dec

2011

Day


SE

Ho
ur

Timing

DAY 1

1,4

8.45
-
9.35

11.25
-
12.15

DAY
2



DAY 3

3,4

10.35
-
11.25

11.25
-
12.15

DAY 4

3

10.35
-
11.25

DAY 5

2

9.35
-
10.25


Location


:
T
ech Park

Faculty Details


Sec.

Name

Office

Office hour

Mail id

SE

Mrs.T.S.Shiny Angel

I
T park

803

Monday
-

Friday

shinyangel@ktr.srmuniv.ac.in




Required Text Books
:

TEXT BOOKS


1.

Stuart Russell, Peter Norvig: “Artificial Intelligence: A Modern Approach”,2
nd

Edition,
Pearson Education, 2007

2.

N.P.Padhy: “Artificial Intelligence and Intelligen
t Systems”,4
th

impression , Oxford
University Press, 2007

3.

Elaine Rich, Kevin Knight: “ Artificial Intelligence” 2
nd

Edition, Tata McGraw
-
Hill, 2006

4.

Patrick Henry Winston: “Artificial Intelligence” 3
rd

Edition, Pearson Education, 2004

5.

Eugene Charniak, Drew
McDermott: “Introduction to Artificial Intelligence”, Pearson
Education 2004



REFERENCE BOOKS

1.

Peter Jackson, ”Introduction to Expert Systems”, 3
rd

Edition, Pearson Education 2007

2.

Patrick Henry Winston,Bartbold Klaus,Paul Horn: “LISP” 3
rd

Edition,Pearson E
ducation

3.

Ivan Bratko: “Prolog:Programming for Artificial Intelligence”,Pearson Education


Web resources

http://library.thinkquest.org/2705

http://www
-
formal.stanford.edu/jmc/whatisai

http://en.wikipedia.org/wiki/Artificial_intelligence


http://ai.eecs.umich.edu

http://www.cee.hw.ac.uk/~alison/ai3notes/subsection2_6_2_3.html


http://starbase.trincoll.edu/~ram/cpsc352/notes/heuristics.html


http://www.macs.hw.ac.uk/~alison/ai3notes/section2_4_3.html

http://www.rbjones.com/rbjpub/logic/log019.htm

http://www.cs.odu.edu/~jzhu/courses/content/logic/pred_logic/intr_to_pred_logic.html

http://www.macs.hw.ac.
uk/~alison/ai3notes/chapter2_5.html


Prerequisite

:

CS0202
-
Principles of Programming Languages





Knowledge in Programming Languages





Knowledge in Computer Fundamentals


Objectives


To provide a strong foundations of fundamental concepts in Artificia
l Intelligence


To get familiar with the various applications of these techniques in Intelligent Systems.


Assessment Details



Cycle Test


I



:

10 Marks

Surprise Test




:

5 Marks

Quiz


: 10

Marks

Cycle Test


II


:

10 Marks

Attendance




:


5 Marks

Term Paper



:

10 Marks


Practical



:

20 marks

Test Schedule

S.No.

DATE

TEST

TOPICS

DURATION

1

Mid September

Cycle Test
-

I

1/3 of syllabus

2 periods

2

End of November

Model Ex
am

Complete
Syllabus

3 Hrs



Outcomes


Students who have successfully completed this course will have full understanding of the
following concepts


Course outcome

Program outcome

To learn


About Knowledge representation and
reasoning methods.





About visual perception and language
Understanding.



About Game playing.



About Expert system and robotics.








An ability to understand Various Ideas in AI


An ability to understand Various Types
of Expert
systems


An ability to understand Various Robot
architectures


Will be able to create Intelligent systems


Will be able to create an expert system






Detailed Session Plan



Representation Of Knowledge And Reasoning Methods :Intelligent Agents



䭮潷汥l来 re灲ese湴慴楯渠


c楲獴
J
佲de爠iog楣i


f湦ere湣e f渠c楲獴
J
l牤r爠iog楣i


Af⁡湤nf湴敲湡氠lep牥獥湴慴楯n

噩獵s氠me牣e灴p潮o䅮搠ianguage 啮摥牳瑡湤楮g㨠噩獩潮o


oec潧湩n楮i 佢橥c瑳t


䑥獣物扩rg f浡来猠


ma牳楮r ianguage㬠iea牮楮i 䅮搠C潭o畮楣u瑩潮o
J

iea牮楮g c牯洠佢獥牶a瑩潮猠


䭮潷汥摧e f渠䱥a牮楮g


p瑡瑩獴楣i氠iea牮楮r⁍e瑨潤t


C潭o畮楣u瑩潮o


me牣e灴p潮

䝡浥mm污y楮gⰠm污湮楮本l 啮摥牳瑡湤楮nⰠC潭o潮ope湳攠
J

䅤癡湣e搠T潰楣猺o䝡浥mm污y楮gⰠmla湮楮nI
啮摥牳瑡湤楮gⰠ䍯I浯渠me湳n

䅢摵A瑩潮Ⱐ啮oe牴r楮
tyⰠ䕸灥牴⁓y獴敭猠䅮搠s潢潴oc猠㨠

t桡琠 f猠 䅢摵A瑩潮o


䅣瑩湧 啮摥r
啮re牴r楮iy


ae晩湩fg⁅x灥牴⁓y獴敭猠
J

o潢潴⁁oc桩hec瑵牥s


Sessi
on
No.

Topics to be covered

Time

(min)

Ref

Teaching
Method

Testing Method

1


Representation Of Knowledge And
Reasoning
Methods
Introduction

50

1

BB

Group discussion

Quiz

2

Intelligent Agents

50

1

BB

Objective type test

Quiz

3,4

Knowledge representation

50

1

BB

Quiz

5,6

First
-
Order Logic

50

1

BB

Quiz

7,8


Inference In First
-
Order Logic

50

1

BB

Quiz


9

AI and Internal R
epresentation

50

1

BB

Quiz

Objective type test

10

Visual Perception And Language
Understanding
-

Introduction

50

2,4

BB

Quiz, Assignment


11,
12


Vision

50

2,4

BB

Group discussion

Comparative study

13,
14


Recognizing Objects

50

2,4

BB

Group discussion

Comparative study

15,
16

Describing Images

50

2,4

BB

Quiz

17

Parsing Language

50

2,4

BB

Quiz

Brain storming

18

Learning And Communication

50

1

BB

Quiz

Surprise Test

19,
20

Learning From Observations

50

1

BB

Group discussion

Quiz

21

Knowledge In Learn
ing

50

1

BB

Group discussion, Quiz

22
-
24

Statistical Learning Methods

50

1

BB

Quiz, Assignment

25
-
26


Communication

50

1

BB

Quiz, Assignment

27

Perception

50

1,3

BB

Group discussion

Quiz

28
-
29

Game Playing

50

1,3

BB

Quiz

Brain storming

30
-

31

Planning

50

1,3

BB

Quiz

Group discussion

31
-

32

Understanding

50

3

BB

Quiz, Comparative
study

33

Common Sense

50

3

BB

Quiz

Surprise Test

34

Advanced Topics: Game Playing,
Planning

50

3

BB

Quiz

Group discussion

35

Advanced Topics: Understanding,
Common Sense

5
0

3

BB

Quiz

Comparative study

36

Abduction, Uncertainty, Expert Systems
And Robotics
-

Introduction


1,3,R1



37

Abduction

50

4

BB

Quiz

Group discussion

38
-

39


Uncertainty

50

1

BB

Quiz

40
-
42


Defining Expert Systems, Expert Systems

50

3,R1

BB

Quiz

Bra
in storming

43
-
45

Robot Architectures


50

1

BB

Quiz


BB


Black Board