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DOC/LP/01/28.02.02
LP-CS 1351
LESSON PLAN
LP: Rev. No: 01
Sub Code & Name : CS1351 – ARTIFICIAL INTELLIGENCE
Date: 02-01-2009
Unit: I
Page 1 of 6
Branch : CS
Semester : VI
UNIT I – INTRODUCTION
8
Intelligent Agents- Agents and environments-Good behavior- The nature of environments-structure
of agents-Problem Solving agents-example problems-Searching for solutions- uninformed search
strategies-avoiding repeated states- searching with partial information.
Objective: To explain the role of agents and how it is related to environment and the way of
evaluating it. Also explains how agents can act by establishing goals, problem solving and
considering sequences of actions that might achieve goals and gives an introduction to searching
strategies.
Session
No
1
Topics to be covered
History and Definition of AI, Foundations
Time
50 min
2
Intelligent Agents - Agents and environmentsGood behavior- the nature of environments
50 min
3
Structure of agents-Problem Solving agents
4
Example problems-Searching for solutions
5
Ref
1,R1,
R2
1
Teaching
Method
BB
BB
1
BB, OHP
50 min
1
BB
Uninformed search strategies- Breadth- first,
depth-first, depth limited search
50 min
1, R3
BB
6
Uninformed search strategies –Iterative
deepening DFS, bi-directional search strategies
50 min
1, R3
BB
7
Avoiding repeated states, searching with
partial information
50 min
1
BB
8
Example problems & Review
50 min
1, R2,
R3
BB, OHP
CAT 1
75 min
DOC/LP/01/28.02.02
LP-CS 1351
LESSON PLAN
LP: Rev. No: 01
Date: 02-01-2009
Sub Code & Name : CS1351 – ARTIFICIAL INTELLIGENCE
Unit: II
Branch : CS
Page 2 of 6
Semester : VI
UNIT II – SEARCHING TECHNIQUES
10
Informed search and exploration- Informed search strategies- heuristic function-Local search
algorithms and optimistic problems- local search in continuous spaces-online search agents and
unknown environments-constraint satisfaction problems (CSP)-Backtracking search and Local
search for CSP- structure of problems-Adversarial search- Games-Optimal decisions in gamesAlpha-Beta pruning-imperfect real-time decision- games that include and element of chance.
Objective: To introduce the various searching techniques, constraint satisfaction problem and
example problems- game playing techniques.
Session
No
9
Topics to be covered
Informed search and exploration- Informed
search strategies, greedy best-first, A*
Algorithm
Memory-bounded heuristic search, heuristic
functions
Local search algorithms and optimization
problems, searching in continuous space
Online search agents and unknown
environments
CSP – backtracking search for CSPs
Time
50 min
Ref
1,R2
Teaching
Method
BB
50 min
1
BB
50 min
1,R3
BB
50 min
1,R3
BB
14
Backtracking search for CSPs, Local search
for CSP- structure of problems
50 min
1
BB
15
Adversarial search- Games-Optimal decisions
in games-minimax algorithm, multiplayer
games
Alpha-beta pruning
Imperfect real time decision, Games that
include an element of chance
Review
50 min
1
OHP
50 min
50 min
1,R3
1
BB
BB
50 min
1, R2,
R3
BB
CAT 2
75min
10
11
12
13
16
17
18
DOC/LP/01/28.02.02
LP-CS1351
LESSON PLAN
LP: Rev. No: 01
Date: 02-01-2009
Sub Code & Name : CS1351 – ARTIFICIAL INTELLIGENCE
Page 3 of 6
Unit: III
Branch : CS
Semester : VI
UNIT III – KNOWLEDGE REPRESENTATION
10
First order logic-representation revisited-Syntax and semantics for first order logic-using first order
logic-Knowledge engineering in first order logic-inference in first order logic-prepositional versus
first order logic-unification and lifting-forward chaining-backward chaining-resolution-knowledge
representation-ontological engineering-categories and objects-actions-simulation and events-mental
events and mental objects.
Objective: To teach the concepts of first order logic and inference in first order logic and
prepositional versus first order logic. Also introduces the concept of forward and backward
chaining and Knowledge representation, categories and objects.
Session
No
19
Topics to be covered
Introduction to Logic, Syntax and semantics of
first order logic
Using first order logic, assertions and queries
in first-order logic, kinship domain, Wumpus
world problem
Knowledge engineering in first order logic
Time
50 min
Ref
1,R3
Teaching
Method
BB
50 min
1,R2
BB, OHP
50 min
1
BB
22
Inference in first order logic- Propositional vs.
first-order inference, Unification and lifting
50 min
1,R2
BB
23
24
Storage and retrieval, Forward chaining
Backward chaining
50 min
50 min
1,R2
1,R2
BB
BB
25
Resolution
50 min
1,R2
BB
26
Knowledge representation - Ontological
engineering, categories and objects
50 min
1
BB
27
Action, situations and events
50 min
1
BB, OHP
28
Mental events and mental objects
50 min
1
BB
29
Review
50 min
1, R2,
R3
BB
CAT 3
75 min
20
21
DOC/LP/01/28.02.02
LP-CS1351
LESSON PLAN
LP: Rev. No: 01
Sub Code & Name : CS1351 – ARTIFICIAL INTELLIGENCE
Date: 02-01-2009
Unit: IV
Page 4 of 6
Branch : CS
Semester : VI
UNIT IV – LEARNING
9
Learning from observations-forms of learning- Inductive learning-Learning decision treesensemble learning-knowledge in learning-logical formulation of learning-explanation based
learning-learning using relevant information-inductive logic programming-statistical learning
methods-learning with complete data-leaning with hidden variable-EM algorithm- Instance based
learning-Neural networks-Reinforcement learning-Passive reinforcement learning-Active
reinforcement learning-Generalization in reinforcement learning.
Objective: To explain the forms of learning, explanation based learning, Inductive logic
programming and statistical learning methods, EM algorithm, Neural Networks- Reinforcement
learning.
Session
No
30
31
32
33
34
35
36
37
38
39
40
Topics to be covered
Introduction, Learning from observations,
Inductive learning
Learning decision trees
Ensemble learning, logical formulation of
learning, Knowledge in learning, explanation
based learning
Learning using relevance information,
inductive logic programming
Statistics learning methods, learning with
complete data
Learning with hidden variables – EM
algorithm
Instance based learning, Introduction to Neural
networks
Neural networks, learning neural network
structures
Reinforcement learning, passive reinforcement
learning
Active reinforcement learning
Generalization in reinforcement learning &
Review
CAT 4
Time
50 min
Ref
1
Teaching
Method
BB
50 min
50 min
1
1
BB, OHP
BB
50 min
1
BB
50 min
1
BB
50 min
1
BB, OHP
50 min
1
BB
50 min
1
BB, OHP
50 min
1
BB
50 min
50 min
1
1
BB
BB
75 min
DOC/LP/01/28.02.02
LP-CS 1351
LESSON PLAN
LP: Rev. No: 01
Date: 02-01-2009
Sub Code & Name : CS1351 – ARTIFICIAL INTELLIGENCE
Unit: V
Branch : CS
Page 5 of 6
Semester : VI
UNIT V – APPLICATIONS
8
Communication-communication as action-formal grammar for a fragment of English-Syntactic
analysis-Augmented grammars-Semantic interpretation-Ambiguity and disambiguation-Discourse
understanding-Grammar induction-Probabilistic language processing- Probabilistic language
models-Information retrieval-Information extraction-Machine translation.
Objective: To learn about the applications of AI in communication, grammar induction and
probabilistic language processing.
Session
No
41
Topics to be covered
Communication - Communication as action, A
formal grammar for a fragment of English
Time
50 min
Ref
1
Teaching
Method
BB
42
Syntactic analysis
50 min
1
BB, OHP
43
Augmented grammars, Semantic interpretation
50 min
1
BB, OHP
44
Semantic interpretation, Ambiguity and
disambiguation
50 min
1
BB, OHP
45
Discourse understanding-Grammar induction
50 min
1
BB
46
Probabilistic language processing Probabilistic language models
50 min
1
BB
47
Information Retrieval and implementation
50 min
1
BB
48
Information Extraction, Machine translation
systems
50 min
1
BB
49
Review
CAT 5
50 min
75 min
1
BB
DOC/LP/01/28.02.02
LP-CS1351
LESSON PLAN
LP: Rev. No: 01
Sub Code & Name: CS1351 – ARTIFICIAL INTELLIGENCE
Branch : CS
Date: 02-01-2009
Page 6 of 6
Semester : VI
Course Delivery Plan
1
2
3
4
5
6
7
8
9
10
11
12
I II
I II
I II
I II
I II
I II
I II
I II
I II
I II
I II
I II
Week
Units
U
1
U
2
U
3
U
4
13
I II
U
5
BOOKS FOR STUDY:
TEXT BOOK
1. Stewart Russell and Peter Norvig. " Artificial Intelligence-A Modern Approach ", 2nd
Edition, Pearson Education/ Prentice Hall of India, 2004
REFERENCES
1.
2.
3.
Nils J. Nilsson, “Artificial Intelligence: A new Synthesis”, Harcourt Asia Pvt. Ltd., 2000.
Elaine Rich and Kevin Knight, “Artificial Intelligence”, 2nd Edition, Tata McGraw-Hill,
2003.
George F. Luger, “Artificial Intelligence-Structures And Strategies For Complex Problem
Solving”, Pearson Education / PHI, 2002.
Prepared by
Approved by
Signature
Name
Designation
G.JANAKASUDHA
Lecturer
Prof. R.NEDUNCHELIAN
HOD, CS
Date
02-01-2009
05-01-2009
CS1351
ARTIFICIAL INTELLIGENCE
3 0 0 100
AIM
Artificial Intelligence aims at developing computer applications, which encompasses perception,
reasoning and learning and to provide an in-depth understanding of major techniques used to
simulate intelligence.
OBJECTIVE



To provide a strong foundation of fundamental concepts in Artificial Intelligence
To provide a basic exposition to the goals and methods of Artificial Intelligence
To enable the student to apply these techniques in applications which involve perception,
reasoning and learning.
UNIT I
INTRODUCTION
8
Intelligent Agents – Agents and environments - Good behavior – The nature of environments –
structure of agents - Problem Solving - problem solving agents – example problems – searching for
solutions – uniformed search strategies - avoiding repeated states – searching with partial
information.
UNIT II
SEARCHING TECHNIQUES
10
Informed search and exploration – Informed search strategies – heuristic function – local search
algorithms and optimistic problems – local search in continuous spaces – online search agents and
unknown environments - Constraint satisfaction problems (CSP) – Backtracking search and Local
search for CSP – Structure of problems - Adversarial Search – Games – Optimal decisions in
games – Alpha – Beta Pruning – imperfect real-time decision – games that include an element of
chance.
UNIT III
KNOWLEDGE REPRESENTATION
10
First order logic – representation revisited – Syntax and semantics for first order logic – Using first
order logic – Knowledge engineering in first order logic - Inference in First order logic –
prepositional versus first order logic – unification and lifting – forward chaining – backward
chaining - Resolution - Knowledge representation - Ontological Engineering - Categories and
objects – Actions - Simulation and events - Mental events and mental objects
UNIT IV
LEARNING
9
Learning from observations - forms of learning - Inductive learning - Learning decision trees Ensemble learning - Knowledge in learning – Logical formulation of learning – Explanation based
learning – Learning using relevant information – Inductive logic programming - Statistical learning
methods - Learning with complete data - Learning with hidden variable - EM algorithm - Instance
based learning - Neural networks - Reinforcement learning – Passive reinforcement learning Active reinforcement learning - Generalization in reinforcement learning.
UNIT V
APPLICATIONS
8
Communication – Communication as action – Formal grammar for a fragment of English –
Syntactic analysis – Augmented grammars – Semantic interpretation – Ambiguity and
disambiguation – Discourse understanding – Grammar induction - Probabilistic language
processing - Probabilistic language models – Information retrieval – Information Extraction –
Machine translation.
TOTAL : 45
TEXT BOOK
Stuart Russell, Peter Norvig, “Artificial Intelligence – A Modern Approach”, 2nd Edition,
Pearson Education / Prentice Hall of India, 2004.
REFERENCES
1. Nils J. Nilsson, “Artificial Intelligence: A new Synthesis”, Harcourt Asia Pvt. Ltd., 2000.
2. Elaine Rich and Kevin Knight, “Artificial Intelligence”, 2nd Edition, Tata McGraw-Hill,
2003.
3. George F. Luger, “Artificial Intelligence-Structures And Strategies For Complex Problem
Solving”, Pearson Education / PHI, 2002.