Download CS0302 COURSE PLAN Cour

 Course Code
Course Title
Semester
Course Time
SRM UNIVERSITY
FACULTY OF ENGINEERING AND TECHNOLOGY
SCHOOL OF COMPUTING
DEPARTMENT OF SWE
COURSE PLAN
: CS0302
: ARTIFICIAL INTELLIGENCE AND EXPERT SYSTEMS
: VI
: June– Dec 2014
Day order
Hour
2,4
5
1
Day 2
Day 3
Day 5
Location
Timing
9.35-10.20 11.25-12.15
1.30-2.20
8.45-9.35
: S.R.M.E.C – University building(12th floor)
Faculty Details
Sec.
B&
C
Name
Mrs.
Office
University
building
Office hour
Mail id
Monday to Friday
[email protected]
N.SNEHALATHA
Required Materials:
TEXT BOOKS:
1. Elaine Rich, “Artificial Intelligence”, 2nd Edition, McGraw Hill, 2005
2. Dan W.Patterson, “ Introduction to AI and ES”, Pearson Education, 2007
REFERENCE BOOKS:
1. Peter Jackson,” Introduction to Expert Systems”, 3rd Edition, Pearson Education, 2007
2. Stuart Russel, Peter Norvig “AI – A Modern Approach”, 2nd Edition, Pearson Education 2007.
ONLINE REFERENCES:
1. http://library.thinkquest.org/2705/
2. http://www-formal.stanford.edu/jmc/whatisai/
3. http://en.wikipedia.org/wiki/Artificial_intelligence
4. http://ai.eecs.umich.edu/
5. http://www.cee.hw.ac.uk/~alison/ai3notes/subsection2_6_2_3.html
6. http://starbase.trincoll.edu/~ram/cpsc352/notes/heuristics.html
7. http://www.macs.hw.ac.uk/~alison/ai3notes/section2_4_3.html
8. http://www.rbjones.com/rbjpub/logic/log019.htm
9. http://www.cs.odu.edu/~jzhu/courses/content/logic/pred_logic/intr_to_pred_logic.html
10. http://www.macs.hw.ac.uk/~alison/ai3notes/chapter2_5.html
Prerequisite
:
NIL
Objectives
• To study the concepts of Artificial Intelligence
• Methods of solving problems using Artificial Intelligence
• Introduce the concepts of Expert Systems and machine learning.
Outcomes
Students who have successfully completed this course will have full understanding of the
following concepts
Course outcome
Program outcome
Artificial Intelligence Overview
Artificial Intelligence Concepts and Algorithms
Expert system Models
Concepts of Artificial Intelligence and Expert
System Concepts.
Examine methods that have emerged from
both fields and proven to be of value in
recognizing patterns and making predictions
from an application perspective.
Assessment Details
Cycle Test – I
Surprise Test – I
Cycle Test – II
Surprise Test – II
Model Exam
:
:
:
:
:
10Marks
8 Marks
10Marks
7 Marks
15 Marks
Test Schedule
S.No.
1
2
3
DATE
TEST
Cycle Test - I
Cycle Test - II
Model Exam
TOPICS
Unit I & II
Unit III & IV
All 5 units
Outcomes
Students who have successfully completed this course will have full
DURATION
2 periods
2 periods
3 Hrs
understanding of the following concepts, Various Ideas in AI ,Various Types
of Expert systems
Detailed Session Plan
UNIT-I
An Introduction to Artificial Intelligence and Production Systems
Sessi
Time
Topics to be
covered
on
Teaching
Ref
(min)
Testing Method
Method
No.
1.
Introduction to Al
50
T1
BB
Group discussion
Quiz
2.
Problem formulation, Problem Definition
50
T1
BB
Objective type test
Quiz
3
Production systems, Control strategies,
50
T1
BB
50
T1
BB
50
R2
BB
50
T1
BB
Quiz
Search strategies.
4
Problem
characteristics,
Production
Quiz
system characteristics
5
6
Specialized production systems
Problem solving methods
Quiz
Quiz
Objective type test
7
Problem graphs, Matching, Indexing and
50
T1
BB
Heuristic functions
8
Quiz
Objective type test
Hill Climbing, Depth first and Breath first,
50
T1
BB
Group discussion
Quiz
9
Constraints
satisfaction
— Related
50
T1
BB
algorithms
10
Measure of performance and analysis of
search algorithms.
Objective type test
Quiz
50
T1
BB
Representation of Knowledge, Characteristics of various Applications, Various aspects
of
Artificial Intelligence and Expert Systems,
11
Gam
playing
—
Knowledge
Group discussion
UNIT-II
e
12
13
representation
Knowledge representation using Predicate
50
T1
BB
50
T1
BB
logic
Introduction to predicate calculus
15
Resolution, Use of predicate calculus
16
Knowledge representation using other
17
logic
50
T1
BB
50
T1
BB
50
T1
BB
50
T1
BB
50
R1,R2
BB
50
T2
BB
Structured representation of knowledge.
UNIT-III
Quiz
Objective type test
Objective type test
Objective type test
Fundamentals Of Expert Systems
21
Basic plan generation systems, Strips
22
Advanced plan generation systems — K
strips
23
Objective type test
Quiz
14
18
19
20
Quiz
Quiz
Quiz
Objective type test
— D Comp. Expert systems
50
T2
BB
Group discussion
Quiz
24
Architecture of expert systems
50
T2
BB
Objective type test
Quiz
25
Roles of expert systems
26
Knowledge Acquisition
27
Meta knowledge Heuristics.
28
Heuristics.
29
50
T2
PP
50
T2
PP
50
T2
PP
50
T2
PP
Quiz
Quiz
Quiz
Objective type test
Quiz
Objective type test
UNIT-IV Knowledge Inference
Knowledge representation
30
50
R1
BB
Group discussion
Quiz
31
32
33
34
Production based system, Frame based
system. Inference
Backward chaining, Forward chaining,
50
R1
BB
50
R1
BB
50
R1
BB
50
R1
BB
50
R1
BB
Objective type test
Quiz
Quiz
35
36
Rule value approach, Fuzzy reasoning
Quiz
37
UNIT-V
Machine Learning
38
Strategic explanations
Quiz
39
40
Why, Why not and how explanations.
Learning
41
Machine learning, adaptive learning.
Quiz
Objective type test
50
R1
BB
Group discussion
42
Quiz
43
Typical expert systems — MYCIN, PIP,
44
INTERNIST, DART, XOON,
Quiz
45
Expert systems shells
Objective type test
Quiz
50
50
R1
R1
BB
BB
Objective type test
Prepared by
Approved by
N.Snehalatha
HOD/SWE