Swami Vivekananda Institute of Science and Technology.

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24 Οκτ 2013 (πριν από 3 χρόνια και 5 μήνες)

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Swami Vivekananda Institute of Science and Technology
.

LECTURE PLAN

Subject Name: Artificial Intelligence
.

Subject Code: 703 C
.

Teacher Name: Anindita Das.


Days
required

Lecture Topic Covered

Remarks

Day 1

Introduction to AI, Comparison with human
intel
ligence, Types of AI, Different types of tasks,
Application, Methods, Turing Machine, and
Limitation.


Day 2

Agents & environment, nature of environment,
structure of agents, goal based agents, utility
based agents, learning agents.


Day 3

Problem charac
teristics, issues in the design of
search programs, solving problems by searching
:problem solving agents.


Day 4

Searching for solutions; uniform search strategies:
breadth first search, depth first search, depth
limited search, bidirectional search.


D
ay 5

Comparing uniform search strategies, introduction
to heuristic search.


Day 5

Greedy best
-
first search, A* search, memory
bounded heuristic search: local search algorithms
& optimization problems: Hill climbing search.


Day 6

Hill climbing search (c
ontd..), simulated
annealing search, local beam search.


Day 7

Games, optimal decisions & strategies in games.


Day 8

The minimax search procedure, alpha
-
beta
pruning.


Day 9

Alpha
-
beta pruning (contd..), additional
refinements, iterative deepening.


Day 10

Knowledge representation issues, representation
& mapping, approaches to knowledge
representation.


Day 11

Knowledge representation, issues in knowledge

representation.


Day 12

Representing simple fact in logic, representing
instant & ISA relation
ship,


Day 13

ISA relationship(contd..), computable functions &
predicates.


Day 14

Resolution, natural deduction.

representing knowledge using rules.


Day 15

Procedural verses declarative knowledge, logic
programming, forward verses backward
reasoning,


Day 16

Forward verses backward reasoning(contd..),
matching, control knowledge.


Day 17

Probabilistic reasoning,
representing knowledge
in an uncertain domain.




Day 18

The semantics of Bayesian networks, Dempster
-
Shafer theory.


Day 19

Fuzzy sets &

fuzzy logics.


Day 20

Fuzzy sets & fuzzy logics(contd..).


Day 21

Planning
overview, components of a planning
system, goal stack planning.


Day 22

Hierarchical planning, other planning techniques.




Day 23

Natural Language processing
introduction,
Sy
ntactic processing, semantic analysis,


Day 24

Natural Language processing
introduction,
Syntactic processing, semantic analysis(contd..)
discourse & pragmatic processing.


Day 25

Problems of AI, AI technique, Tic
-

Tac
-

Toe
problem, genetic algorithm.


Day 26

Learning :
forms of learning, inductive learning,
learning decision trees, explanation based
learning, learning using relevance information.


Day 27

Neural net learning & genetic learning.


Day 28

Expert Systems:
representing and using domain
kno
wledge.


Day 29

Expert system shells, knowledge acquisition.


Day 30

Production system, problem characteristics, issues
in the design of search programs.



NOTE:

Some more classes may required f
or discussing question pattern
s

of AI.

It is considered tha
t
PROLOG and LISP will be discussed in laboratory.