FT/GN/68/01/23.01.16
SRI VENKATESWARA COLLEGE OF ENGINEERING
COURSE DELIVERY PLAN - THEORY
Page 1 of 6
LP: CS6659
Department of Computer Science and Engineering
B.E/B.Tech/M.E/M.Tech : B.E
Rev. No: 01
Date: 27/01/2016
Regulation: 2013
PG Specialisation
: --
Sub. Code / Sub. Name
: CS6659 – ARTIFICIAL INTELLIGENCE
Unit
:I
Unit Syllabus: Introduction to AI - Problem formulation, Problem Definition - Production systems, Control strategies,
Search strategies. Problem characteristics, Production system characteristics - Specialized production system - Problem
solving methods - Problem graphs, Matching, Indexing and Heuristic functions - Hill Climbing - Depth first and
Breadth first, Constraint satisfaction - Related algorithms, Measure of performance and analysis of search algorithms.
Objective:


To understand the concepts of Artificial Intelligence
To learn the methods of solving problems using Artificial Intelligence
Session
No *
1
2
3
Topics to be covered
Introduction to AI , Problem formulation for Tic-Tac-Toe,
Question answering
Problem definition as a state space search for Chess and Water Jug
problem
Production systems - Control strategies, Search strategies –
Breadth First search, Depth-First search, Heuristic search
Ref
Teaching
Aids
T1(Ch 1 : 1 – 20)
R2 (Ch 1: 29: 58)
LCD
T1(Ch 1 : 25 – 30)
R2 (Ch 3 : 87 - 97)
LCD
T1 (Ch 2 : 30 – 36)
T1(Ch 2 : 36 – 45)
LCD
4
Problem characteristics, Production system characteristics
and Specialized production system
5
Issues in the design of search programs, Problem characteristics
for various toy problems
6
Problem solving methods – Matching and Indexing
7
Heuristic Search Techniques - Generate-and-Test, Hill Climbing
Best-First search : OR Graphs, A* algorithm
8
Problem Reduction - AND-OR graphs, AO* algorithm
T1(Ch 3 : 68 – 72)
R2(Ch 5 : 165 – 186)
LCD
9
Constraint satisfaction for Cryptarithmetic problem, Measure of
performance and analysis of search algorithms
T1(Ch 3 : 72 – 74)
R2(Ch 3 : 109 – 111)
LCD
T1(Ch 2 : 45 – 49)
T2 (Ch 7 : 126 – 145)
T1(Ch 6 : 138 – 142)
T2 (Ch 10 : 188 – 211
& Ch 11 : 211 - 227)
T1(Ch 3 : 50 – 57)
R2(Ch 3 : 101 – 106)
LCD
LCD
LCD
LCD
Content beyond syllabus covered (if any): Uninformed search strategies : Depth-limited search and Bidirectional
search
* Session duration: 50 minutes
FT/GN/68/01/23.01.16
SRI VENKATESWARA COLLEGE OF ENGINEERING
COURSE DELIVERY PLAN - THEORY
Page 2 of 6
Sub. Code / Sub. Name: CS6659 – ARTIFICIAL INTELLIGENCE
Unit : II
Unit Syllabus : Game playing - Knowledge representation, Knowledge representation using Predicate logic,
Introduction to predicate calculus, Resolution, Use of predicate calculus, Knowledge representation using other
logic - Structured representation of knowledge.
Objective:

To represent knowledge for problem solving and game playing using various logics.
Topics to be covered
10
11
12
13
14
Game playing – Overview, Minimax search procedure, Adding
Alpha-beta Cutoffs, Additional refinements, Iterative Deepening
Knowledge representation – Representations and Mappings,
Approaches to Knowledge representation, Issues in Knowledge
representation, The Frame problem
Knowledge representation using Predicate logic – Representing
simple facts in logic, Representing Instance and ISA
relationships, Computable Functions and Predicates
Ref
Teaching
Aids
T1(Ch 12 : 231 – 247)
LCD
T1(Ch 4 : 79 – 97)
LCD
T1(Ch 5 : 98 – 107)
LCD
T2 (Ch 4 : 55 – 73)
LCD
T1(Ch 5 : 108 – 126)
T2(Ch 4 : 66 – 73)
LCD
Knowledge representation using Predicate logic – Resolution
Knowledge representation using Predicate logic – Unification and
Natural deduction
15
Representing knowledge using Rule – Forward and Backward
Reasoning
T2 (Ch 5 : 80 – 106)
LCD
16
Knowledge representation using other logic – Nonmonotonic
Reasoning and logics, Truth Maintenance System
T2 (Ch 7 : 126 – 135)
LCD
17
Structured representation of knowledge – Frames and semantic
networks
T2(Ch 7 : 136 – 140)
LCD
18
Structured representation of knowledge – Conceptual
dependencies and scripts
T2(Ch 7 : 142 – 145)
LCD
Content beyond syllabus covered (if any): Description logic
* Session duration: 50 mins
FT/GN/68/01/23.01.16
SRI VENKATESWARA COLLEGE OF ENGINEERING
COURSE DELIVERY PLAN - THEORY
Page 3 of 6
Sub. Code / Sub. Name: CS6659 – ARTIFICIAL INTELLIGENCE
Unit : III
Unit Syllabus: Knowledge representation - Production based system, Frame based system. Inference - Backward
chaining, Forward chaining, Rule value approach, Fuzzy reasoning - Certainty factors, Bayesian Theory - Bayesian
Network - Dempster - Shafer theory
Objective:
 To carry out knowledge inferences over production based and frame based system.
 To represent and handle uncertainty and vagueness
Session
No *
Topics to be covered
19
Knowledge representation : Production based system and
Inferencing
20
Knowledge representation : Production based system Backward chaining and Forward chaining
21
Knowledge representation : Frame based system
22
Knowledge representation : Frame based system – Inference
and Reasoning
23
Statistical reasoning – Probability and Baye’s Theorem, Certainty
factors and Rule value approach
Ref
R4
T1(Ch 6 : 129 – 135)
R4
R4
T1(Ch 8 : 172 – 178)
Teaching
Aids
LCD
LCD
LCD
LCD
LCD
T1(Ch 8 : 179 – 181)
LCD
Dempster - Shafer theory
T1(Ch 8 : 181 – 184)
R2 (Ch 14 : 551 – 554)
LCD
26
Fuzzy logic – Introduction and Terminology
T1(Ch 8 : 184 – 186)
R2 (Ch 14 : 554 – 556)
LCD
27
Fuzzy reasoning
T1(Ch 22 : 445 –456)
R2(Ch 14 : 554 – 556)
LCD
24
25
Statistical reasoning - Bayesian Theory and Bayesian Network
Content beyond syllabus covered (if any): Inference in Bayesian networks
* Session duration: 50 minutes
FT/GN/68/01/23.01.16
SRI VENKATESWARA COLLEGE OF ENGINEERING
COURSE DELIVERY PLAN - THEORY
Page 4 of 6
Sub. Code / Sub. Name: CS6659 – ARTIFICIAL INTELLIGENCE
Unit : IV
Unit Syllabus : Basic plan generation systems - Strips - Advanced plan generation systems – K strips -Strategic
explanations -Why, Why not and how explanations. Learning - Machine learning, adaptive Learning
Objective:

To introduce the concepts of planning and machine learning
Session
No *
Topics to be covered
Ref
Teaching
Aids
28
Planning – Overview, Components
of a Planning System, Example : The Blocks World Problem solving
using Planning
T1 (Ch 13 : 247 – 255)
R2(Ch 11: 403 – 437)
LCD
Planning – Goal Stack Planning
T1(Ch 13 : 255 – 269)
R2(Ch 12: 445– 483)
LCD
30
Nonlinear planning using constraint posting, Hierarchical Planning,
Strips
T1(Ch 13 : 255 – 269)
R2(Ch 11: 405– 410)
31
Advanced plan generation systems - K strips
R4
32
Strategic explanations -Why, Why not and how explanations
R4
33
Learning – Introduction, Rote learning, Learning by taking
advice, Learning in Problem-solving, Learning from Examples
34
Learning – Explanation-based learning, Discovery, Analogy,
Formal learning theory
35
Learning – Neural Net learning and Genetic learning
36
Adaptive Learning
29
Content beyond syllabus covered (if any): Ensemble learning
* Session duration: 50 mins
T1(Ch 17 : 347 – 364)
R2(Ch 18 : 677 – 700)
,R3
T1(Ch 17 : 364 – 373)
R2(Ch 19 : 716 – 722
,R3
T1(Ch 17 : 373 – 375)
R2(Ch 20 : 764 – 777)
R2(Ch 4 : 144 – 147)
R4
LCD
LCD
LCD
LCD
LCD
LCD
LCD
FT/GN/68/01/23.01.16
SRI VENKATESWARA COLLEGE OF ENGINEERING
COURSE DELIVERY PLAN - THEORY
Page 5 of 6
Sub. Code / Sub. Name: CS6659 – ARTIFICIAL INTELLIGENCE
Unit : V
Unit Syllabus : Expert systems - Architecture of expert systems, Roles of expert systems - Knowledge Acquisition –
Meta knowledge, Heuristics. Typical expert systems - MYCIN, DART, XCON, Expert systems shells.
Objective:

To introduce the concepts of Expert Systems with case studies for various applications.
Session
No *
Topics to be covered
37
Expert systems – Introduction and Characteristics
38
Knowledge acquisition - Architecture of expert systems
39
Expert systems - Roles of expert systems
40
Knowledge Acquisition – Meta knowledge, Heuristics
41
Expert systems – MYCIN - Architecture
42
Expert systems – MYCIN - Knowledge Acquisition
Ref
Teaching
Aids
R1 (Ch 1 : 1-4)
LCD
T2 (Ch 15 : 330 – 347)
R1 (Ch 1 : 4 – 9)
R1 (Ch 1 : 4 – 9)
LCD
LCD
R1 (Ch 1 : 4 – 9)
LCD
R1(Ch 3 :38 – 57)
LCD
R1(Ch 3: 38 – 57)
LCD
43
Expert systems – DART – Architecture and Knowledge
Acquisition
R1(Ch 19:374 – 377)
LCD
44
Expert systems – XCON - Architecture and Knowledge
Acquisition
R1(Ch 16:308 – 315)
T2(Ch 15 : 328)
LCD
45
Expert systems shells
T1 (Ch 20:424 – 427)
Content beyond syllabus covered (if any): Pathfinder expert system, DIAVAL expert system
* Session duration: 50 mins
LCD
FT/GN/68/01/23.01.16
SRI VENKATESWARA COLLEGE OF ENGINEERING
COURSE DELIVERY PLAN - THEORY
Page 6 of 6
Sub Code / Sub Name: CS6659 – ARTIFICIAL INTELLIGENCE
TEXT BOOKS:
1. Kevin Night and Elaine Rich, Nair B., “Artificial Intelligence (SIE)”, Mc Graw Hill- 2008.
(Units-I,II,VI & V)
2. Dan W. Patterson, “Introduction to AI and ES”, Pearson Education, 2007. (Unit-III).
REFERENCES:
1. Peter Jackson, “Introduction to Expert Systems”, 3rd Edition, Pearson Education, 2007.
2. Stuart Russel and Peter Norvig “AI – A Modern Approach”, 2nd Edition, Pearson Education
2007.
3. Deepak Khemani “Artificial Intelligence”, Tata Mc Graw Hill Education 2013.
4. http://nptel.ac.in
Prepared by
Approved by
Dr.R.Jayabhaduri
Dr.C.Jayakumar
Designation
Associate Professor
Professor & HOD/CS
Date
Remarks *:
02/01/2017
02/01/2017
Signature
Name
Remarks *:
* If the same lesson plan is followed in the subsequent semester/year it should be mentioned and
signed by the Faculty and the HOD