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DOC/LP/01/28.02.02
LP-CS2351
LESSON PLAN
LP: Rev. No: 00
Sub Code & Name: CS2351–ARTIFICIAL INTELLIGENCE
Unit : I
Branch : CS
Date: 26/12/2012
Semester : VI
Page 1 of 6
UNIT I PROBLEM SOLVING
9
Introduction – Agents – Problem formulation – uninformed search strategies – heuristics –
informed search strategies – constraint satisfaction
Objective: To introduce Artificial intelligence, to learn the basics of designing intelligent agents
that can solve general purpose problems and to explore the idea of problem solving by several
search strategies
Session
No
1
2
3
4
5
6
7
8
9
10
Topics to be covered
Introduction
to
Artificial
Intelligence,
Foundations and History of Artificial
Intelligence
Intelligent Agents – Agents and environment.
Concept of Rationality, Nature of environments
and Structure of agents
Problem solving agents - Problem formulation
with suitable examples, searching for solutions
Uninformed search strategies – Breadth-first
search, Uniform-cost search, depth-first search,
Depth-limited search
Uninformed search strategies – Iterative
deepening depth-first search, Bidirectional
search, avoiding repeated states
Informed search strategies – Greedy best first
search, A* search
Informed search strategies – Memory-bounded
heuristic search, Heuristic Functions
Local search algorithms and optimization
problems – Hill climbing, Simulated Annealing
and Local beam search
Local search algorithms and optimization
problems – Genetic algorithms, Local search in
continuous spaces
Constraint satisfaction problems – Backtracking
and local search, Local search for CSP, Review
Time in
min
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DOC/LP/01/28.02.02
LP-CS2351
LESSON PLAN
LP: Rev. No: 00
Sub Code & Name: CS2351–ARTIFICIAL INTELLIGENCE
Date: 26/12/2012
Unit : II
Page: 2 of 6
Branch : CS
Semester : VI
UNIT II LOGICAL REASONING
9
Logical agents – propositional logic – inferences – first-order logic – inferences in first order
logic – forward chaining – backward chaining – unification – resolution
Objective: To illustrate the basic concepts of logic and knowledge-based agents and to discuss
the representation of knowledge and the reasoning process. Also to define effective procedures
for answering questions by making inferences in first-order logic
Session
No
11
12
13
14
15
16
17
18
19
20
21
22
Topics to be covered
Logical agents – Knowledge based agents,
Wumpus world
Logic
Propositional logic – Syntax, Semantics,
knowledge base, Inference
Reasoning patterns in propositional logic
Agents based on propositional logic
First-order logic – Representation, Syntax and
Semantics of FOL,
First –Order logic- Assertions and queries,
Kinship domain, Numbers, sets and lists
First-order logic representation for Wumpus
world
Inference in First-order logic - Propositional
Vs First order inference, Unification and
lifting
Inference in First-order logic - Forward
chaining
Inference in First-order logic - Backward
Chaining
Inference in First-order logic - Resolution
Time
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DOC/LP/01/28.02.02
LP-CS2351
LESSON PLAN
LP: Rev. No: 00
Sub Code & Name: CS2351–ARTIFICIAL INTELLIGENCE
Date: 26/12/2012
Unit : III
Page
Branch : CS
Semester : VI
3
of 6
UNIT III PLANNING
9
Planning with state-space search – partial-order planning – planning graphs – planning and
acting in the real world
Objective: To introduce the most basic concepts, representations and algorithms for planning, to
explain the method of achieving goals from a sequence of actions (planning) and how better
heuristic estimates can be achieved by a special data structure called planning graph. To
understand the design of agent architectures to deal with uncertain environments
Session
No
23
24
25
26
27
28
29
30
31
Topics to be covered
Planning with state-space search – Forward
state-space search, Backward state-space
search
Partial-order planning – Concept with an
example and heuristics for
Partial-order
planning
Planning graphs Planning graphs for
heuristic estimation, Graph plan algorithm
Planning and acing in the real world – Time,
schedules and resources
Planning and acing in the real world –
Hierarchical Task network planning
Planning and acing in the real world –
Planning and acting in nondeterministic
domains
Planning and acing in the real world –
Conditional Planning in fully and partially
observable environments
Planning and acing in the real world –
Continuous Planning
Planning and acing in the real world –
Multiagent Planning
Time
In min
50
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DOC/LP/01/28.02.02
LP-CS2351
LESSON PLAN
LP: Rev. No: 00
Sub Code & Name: CS2351–ARTIFICIAL INTELLIGENCE
Date: 26/12/2012
Unit : IV
Page
Branch : CS
Semester : VI
4
of 6
UNIT IV UNCERTAIN KNOWLEDGE AND REASONING
9
Uncertainty – review of probability - probabilistic Reasoning – Bayesian networks – inferences
in Bayesian networks – Temporal models – Hidden Markov models
Objective: To understand the concept of uncertainty and to learn the syntax and semantics of
probability theory and Bayesian networks and other approaches to uncertain reasoning
Session
No
32
33
34
35
36
37
38
39
40
41
Topics to be covered
Uncertainty – Acting under Uncertainty - handling uncertain knowledge, Uncertainty
and rational decisions
Uncertainty – Basic Probability Notation –
Propositions, Atomic events, Prior probability,
and The Axioms of probability
Uncertainty – Inference using full joint
distribution, Independence, Bayes’ rule and its
use
Probabilistic Reasoning – Representing
knowledge in an Uncertain domain
Probabilistic Reasoning - Semantics of
Bayesian Networks
Probabilistic Reasoning – Exact Inference in
Bayesian networks
Probabilistic Reasoning – Approximate
Inference in Bayesian networks
Probabilistic Reasoning – Other approaches to
Uncertain reasoning (Rule-based methods for
uncertain reasoning, Dempster-Shafe theory,
Fuzzy sets and fuzzy logic)
Probabilistic Reasoning over time – Inference
in temporal models
Hidden Markov models
Time in
min
50
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DOC/LP/01/28.02.02
LP-CS2351
LESSON PLAN
LP: Rev. No: 00
Sub Code & Name: CS2351–ARTIFICIAL INTELLIGENCE
Date: 26/12/2012
Unit : V
Page 5 of 6
Branch : CS
Semester : VI
UNIT V LEARNING
9
Learning from observation - Inductive learning – Decision trees – Explanation based learning –
Statistical Learning methods - Reinforcement Learning.
Objective: To understand the basic concepts of several learning techniques
Session
No
42
43
44
45
46
47
48
49
50
51
Topics to be covered
Learning from observation – Forms of learning,
Inductive learning
Learning from observation - Learning Decision
trees
Knowledge in learning - Explanation based
learning
Statistical Learning methods – Statistical
learning, Learning with complete data
Statistical Learning methods – Learning with
hidden variables (EM algorithm)
Statistical Learning methods – Instance based
learning
Statistical Learning methods – Neural Networks
(Network structures, Single layer feed-forward
neural network, Multilayer feed-forward neural
network)
Statistical Learning methods – Kernel Machines
Reinforcement Learning – Introduction, Passive
Reinforcement Learning
Reinforcement Learning- Active Reinforcement
Learning, Generalization in Reinforcement
Learning, Policy search, Review
Time
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50
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DOC/LP/01/28.02.02
LP-CS2351
LESSON PLAN
LP: Rev. No: 00
Date: 26/12/2012
Sub Code & Name: CS2351–ARTIFICIAL INTELLIGENCE
Branch : CS
Page
Semester : VI
6 of 6
Course Delivery Plan
Weeks 1
2
I
3
4
II
5
6
7
8
III
9
10
IV
11
12
V
13
Units
BOOKS FOR STUDY:
TEXT BOOK:
1. S. Russel and P. Norvig, “Artificial Intelligence – A Modern Approach”, Second Edition,
Pearson Education, 2003.
REFERENCES:
1. David Poole, Alan Mackworth, Randy Goebel,”Computational Intelligence: a logical
approach”, Oxford University Press, 2004.
2. G. Luger, “Artificial Intelligence: Structures and Strategies for complex problem solving”,
Fourth Edition, Pearson Education, 2002.
3. J. Nilsson, “Artificial Intelligence: A new Synthesis”, Elsevier Publishers, 1998.
Prepared by
Approved by
Signature
Name
Ms.R.Gayathri, Ms.S.Anitha
Dr. T. K Thivakaran
Designation
Assistant Professor/CS
HOD, Department of CS
Date
26/12/2012
26/12/2012