Department of CS/IT
ARTIFICIAL INTELLIGENCE
(IT-833)
Semester: 8th
(Computer science department)
Name of Faculty: Rahul Raghuwanshi
Department of CS/IT
INDEX
Chapter-1: Scheme & Syllabus
Chapter-2: Theory Lecture Plan (50-60 Lectures)
Chapter-3: Practical Lecture Plan / Lab Demo (15 Lectures)
Chapter-4: Theory Assignment (At Least 20 Questions)
Chapter-5: Lab Assignment (At Least 15 Questions)
Chapter-6: RGPV Papers (At least 2 Years Old Question Papers)
Chapter-7: Viva-Voce Questions (30-50 Questions)
Chapter-8: Web Reference(S)
Chapter-9: Unit-wise Objective Tutorial Sheet
Chapter-10: Unit-wise Subject Tutorial Sheet
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Chapter-1 : Scheme & Syllabus
Category
of
Course
Title
Course
Code
Artificial
Intelligence
IT- 833
Credits-4C
Theory
Paper
Course
DCO(E)III
L
T
P Max. Marks-100
5
2
-
Min. Marks-35
Duration 3 hrs
Branch : Information Technology, VIII Semester
Course: Artificial Intelligence
Unit I: Meaning and definition of artificial intelligence, Various types of production systems,
Characteristics of production systems, Study and comparison of breadth first search and depth first
search. Techniques, other Search Techniques like hill Climbing, Best first Search. A* algorithm, AO*
algorithms etc, and various types of control strategies.
Unit II: Knowledge Representation, Problems in representing knowledge, knowledge representation
using propositional and predicate logic, comparison of propositional and predicate logic, Resolution,
refutation, deduction, theorem proving, inferencing, monotonic and non-monotonic reasoning.
Unit III: Probabilistic reasoning, Baye's theorem, semantic networks, scripts, schemas, frames,
conceptual dependency, fuzzy logic, forward and backward reasoning.
Unit IV: Game playing techniques like minimax procedure, alpha-beta cut-offs etc, planning, Study
of the block world problem in robotics, Introduction to understanding and natural languages
processing.
Unit V: Introduction to learning, Various techniques used in learning, introduction to neural
networks,applications of neural networks, common sense, reasoning, some example of expert
systems.
References:

T1: Rich E and Knight K, “Artificial Intelligence”, TMH, New Delhi.
R1: Nelsson N.J., “Principles of Artificial Intelligence”, Springer Verlag, Berlin
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Chapter-2: Theory Lecture Plan (50-60 Lectures)
Unit
No.
Lecture
Nos.
Topics to be covered
Ref.
book
T1
4-5
Meaning and definition of artificial intelligence, Various types of production
systems, Characteristics
of production systems
Study and comparison of breadth first search and depth first search.
6-7
Techniques, other Search Techniques like hill Climbing, Best first Search
T1
8-10
A* algorithm, AO* algorithms etc, and various types of control strategies.
T1
11-14
Knowledge Representation, Problems in representing knowledge, knowledge
representation using propositional and predicate logic
comparison of propositional and predicate logic
Resolution, refutation,deduction, theorem proving, inferencing, monotonic
and non-monotonic reasoning
T1
1-3
T1
1
2
15-16
17-19
3
20-21
22-24
25-26
27-29
Probabilistic reasoning
Baye's theorem, semantic networks
scripts, schemas, frames
Conceptual dependency, fuzzy logic, forward and backward reasoning.
R1
R1
R1
T1
30-33
Game playing techniques like minimax procedure, alpha-beta cut-offs etc
T1
34-36
37-39
planning, Study of the block world problem in robotics
Introduction to understanding and natural languages processing.
T1
T1
40-42
43-46
47-50
Introduction to learning, Various techniques used in learning,
introduction to neural networks, applications of neural networks
common sense, reasoning, some example of expert systems.
T1
R1
R1
4
5
T1
T1
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Chapter-3: Practical Lecture Plan / Lab Demo (15 Lectures)
Note: Not Applicable
Chapter-4: Theory Assignment (At Least 20 Questions)
Q.1) Define intelligence? Why the term Artificial Intelligence ?
Q.2) What are the different approaches in defining artificial intelligence?
Q.3) Define Depth Search and Breadth first Search with example?
Q.4) What is A* seaching?
Q.5) Define A* search properties?
Q.6) Is hill climbing guaranteed to find a solution to the n-queens problem ?
Q.7) Consider a knowledge base KB that contains the following propositional logic
Sentences:
Q═> P
P═ ⌐Q
QVR
a) Construct a truth table that shows the truth value of each sentence in KB and
Indicate the models in which the KB is true.
b) Does KB entail Q═> R? Extend the truth table and use the definition of entailment to
justify your answer.
Q.8) Define Resolution, refutation, deduction?
Q.9) What is monotonic and non-monotonic reasoning?
Q.10) Define Biological and Artificial Neural network?
Q.11) Assume a learning problem where each example is represented by four attributes. Each
attribute can take two values in the set {a,b}. Run the candidate elimination algorithm on the following
examples and indicate the resulting version space. What is the size of the space?
((a, b, b, a), +)
((b, b, b, b), +)
((a, b, b, b), +)
((a, b, a, a), -)
Q.12) Define Perceptron?
Q.13) Consider the following short story:
“John went to the diner to eat lunch. He ordered a hamburger. But John wasn't very hungry so he
didn't _nish it. John told the waiter that he wanted a doggy bag. John gave the waiter a tip. John
then went to the hardware store and home”.
Each inference below is based on a plausible interpretation of the story. For each inference, briefly
explain whether that inference was primarily based on syntactic, semantic, pragmatic, discourse, or
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world knowledge. (Do not answer world knowledge unless none of the other categories are
appropriate.)
(a)John is the person who ordered a hamburger.
(b) John wasn't just stating a fact that he desired a doggy bag, but was requesting that the
waiter bring him a doggy bag.
(c) John went to the hardware store and then went to his house. (As opposed to going to
a hardware store and a hardware home.)
(d) John gave the waiter some money as a gratuity. (As opposed to giving him a suggestion or
hint.)
(e) John was wearing clothes
Q.14) If a machine passes the Turing Test is it considered intelligent? Why?
Q.15) In which situation should the Turing test be used?
Q.16) In which situation should the Turing test be used?
Q.17) We would like to solve the Boolean Satisfiability problem (SAT) using a greedy hill-climbing
algorithm. Each state corresponds to a complete assignment of T or F to each Boolean variable. The
successor operator Succ(s) generates all neighboring states of s, which we define as all total
assignments which differ by exactly one variable’s truth value. So, for example, given the state with
assignments {A ← true, B ← false}, the neighboring states would be {A ← false, B ← false} and {A
← true, B ← true}. We can set the evaluation of a state to be the number of clauses that are satisfied,
given the assignment of the state. This algorithm is usually called GSAT. Assume that ties in the
evaluation function are broken randomly
(a)
If you have n Boolean variables, how many neighboring states does the Succ(s) function
produce?
(b)
What is the total size of the search space, i.e., how many possible states are there in total?
Assume again that there are n variables.
Consider the following problem containing 6 clauses (in parentheses) and 5 variables:
(X ∨ Y ) ∧ (¬X ∨ Z ) ∧ (¬Y ∨ Z ) ∧ (¬X ∨ ¬Z ) ∧ (X ∨ U ) ∧ (X ∨ V )
(c)
From the starting state (X=F, Y=F, Z=T, U=F, V=F), what is the next state reached by hillclimbing, or explain why there is no successor state. Will a global optimal solution be found
by hill-climbing from this initial state?
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(d) Consider the following problem containing 4 clauses and 3 variables:
(¬A ∨ B ∨ C)  (A ∨ ¬B ∨ C)  (A ∨ B ∨ ¬C)  (A ∨ B ∨ C)
Come up with a non-goal state (i.e., a non-satisfying assignment) that is on a plateau in our
hill-climbing space (fill in T or F for each variable):
A =
B =
C =
Q.18)Define minimax procedure?
Q.19)Define alpha-beta cut-offs?
Q.20)What is natural language processing?
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Chapter-5: Lab Assignment (At Least 15 Questions)
Note: Not Applicable
Chapter-6: RGPV Papers (At least 2 Years Old Question Papers)
Note: Not available right now. It will be included soon.
Chapter-7: Viva-Voce Questions (30-50 Questions)
Note: Not Applicable
Chapter-8: Web Reference(s)
http://nptel.iitm.ac.in/courses/Webcourse-contents/IIT Kharagpur/Artificial intelligence
Chapter-9: Unit-wise Objective Tutorial Sheet
Note: Not available right now. It will be included soon.
Chapter-10: Unit-wise Subject Tutorial Sheet
Note: Not available right now. It will be included soon.