USN
1 P E
PESIT Bangalore South Campus
Hosur road, 1km before Electronic City, Bengaluru -100
Department of Computer Science And Engineering
INTERNAL ASSESSMENT TEST – 2
Date
Code & Subject
: 5-04-2017
: 16SCS241 ARTIFICIAL INTELLIGENCE AND AGENT
Max Marks
Semester
:
:
40
2nd
Time
:
08:30 AM to 10:00AM
TECHNOLOGY
Name of Faculty:
: SUDEEPTA MISHRA
Note: All questions are compulsory
1. a. Explain Baye’s theorem. Identify its casual and diagnostic directions.
Ans: Bayes' theorem describes the probability of an event, based on prior knowledge of conditions
that might be related to the event. Bayes' theorem is stated mathematically as the following equation
P(cause | effect) = [P(effect | cause) P(cause)] / [P(effect) ]
Or
Students can also write
P(H|E) = probability of hypothesis H given that we have observed evidence E
P(Hi/E) = probability of hypothesis Hi is true under the evidance E
P(E|Hi) = pribability that we observe evidance E given that hypothesis Hi is true
p(Hi) = a priori robability that hypothesis is true in absence of any specific evidance
k= number of possible hypothesis
To compute P(Hi/E), we have to take into account the prior probability of H (the probability that
we would assign to H if we had no evidence) and the extent to which E provides evidence of H.
P(cause | effect) or P(A|B) or P(Hi/E) is the diagnostic direction.
P(effect | cause) or P(B|A) or P(E|Hi) is casual direction.
M.Tech 2ND SEM
3
USN
1 P E
PESIT Bangalore South Campus
Hosur road, 1km before Electronic City, Bengaluru -100
Department of Computer Science And Engineering
b. An entomologist spots what might be a rare subspecies of beetle, due to the pattern on its back. In 5
the rare subspecies, 98% have the pattern. In the common subspecies, 5% have the pattern. The rare
subspecies accounts for only 0.1% of the population. How likely is the beetle having the pattern to
be rare, or what is P(Rare | Pattern)?
Ans:
2. a. What is Bayesian network? What are its benefits over Baye’s theorem?
Ans: The basic idea of Bayesian Network is knowledge in the world is modular. Most events are
conditionally independent of other events. Adopt a model that can use local representation to allow
interactions between events that only affect each other. The main idea is that to describe the real
world it is not necessary to use a huge list of joint probabilities table in which list of
probabilities of all conceivable combinations of events. Some events may only be unidirectional
others may be bidirectional events may be casual and thus get chained tighter in network.
Benefits:
 It can save considerable amounts of memory, if the dependencies in the joint distribution are
sparse.
 It reduces the time complexity in computing the diagnostic probability.
 It is intuitively easier for a human to understand direct dependencies.
M.Tech 2ND SEM
4
USN
1 P E
PESIT Bangalore South Campus
Hosur road, 1km before Electronic City, Bengaluru -100
Department of Computer Science And Engineering
b. Consider the following facts
i) Sprinklers were on the last night. ii) Grass is wet. iii) It rained last night
Show the flow of constraints and causality graph
Ans:
(a) flow of constraints and (b) causality graph
3. a. What are semantic networks? Represent the following statement in semantic network
“Every dog has bitten every mail carrier.”
Ans: Semantic Nets were originally designed as a way to represent the meaning of English words.
The main idea is that the meaning of a concept comes from the ways in which it is connected to
other concepts. The information is stored by interconnecting nodes with labeled arcs. Semantic nets
initially we used to represent labeled connections between nodes.
M.Tech 2ND SEM
4
8
USN
1 P E
PESIT Bangalore South Campus
Hosur road, 1km before Electronic City, Bengaluru -100
Department of Computer Science And Engineering
4. a. Define certainty factor (CF[h,e]).
2
Ans: A certainty factor (CF[h,e]) is defined in terms of two components:
MB [h, e]:
A measure between 0 & 1 of belief in hypothesis h given the evidence e. MB measures the
extent to which the evidence supports the hypothesis. MB=0, if the evidence fails to support
hypothesis
MD [h, e]:
A measure between 0 & 1 of disbelief in hypothesis h given by the evidence ‘e’ MD
measures the extent to which the evidence does not support hypothesis. MD=0, if the
evidence supports the hypothesis
we can define the certainty factor CF[h,e] = MB [h, e] - MD [h, e]
b Suppose we make an initial observation that confirms our belief in hypothesis h with MB=0.3. Then 2
MD[h,s1]=0, CF[h,s1]=0.3 with respect to observation s1. Now we make a second observation s2,
which also confirms hypothesis h, with MD[h,s2]=0.2. Calculate CF[h,s1^s2].
c. Write MB for conjunction and disjunction of two hypotheses h1 and h2
Ans:
MB[hl \/ h2,e ] = max(MB[ht,e],MB[h2,e])
MB[hl /\ h2,e ] = min(MB[ht,e],MB[h2,e])
M.Tech 2ND SEM
2
USN
1 P E
PESIT Bangalore South Campus
Hosur road, 1km before Electronic City, Bengaluru -100
Department of Computer Science And Engineering
d. In a medical diagnosis we have four possible diseases Θ={Allergy, Cold, Flu, Pneumonia}, the
measure of belief in presence of Fever is
Fever ⇒ m1 ({Cold, Flu, Pneumonia}) = 0.6 , m1 (Θ) = 0.4
Now suppose we observe Running nose and its measure of belief independent of Fever is
Runny nose ⇒ m2 ({Allergy, Flu, Cold}) = 0.8 , m2 (Θ) = 0.2
Find the measure of belief in presence of both Fever and Running nose together.
Ans:
With no evidence we start with m1(Θ) = 1 and m1(X≠Θ) = 0.
Now suppose, we get evidence of Fever and of Runny nose, and individually they imply
Fever ⇒ m1({Flu, Cold, Pneu}) = 0.6 , m1(Θ) = 0.4
Runny nose ⇒ m2({Allergy, Flu, Cold}) = 0.8, m2(Θ) = 0.2
5
Then these two pieces of evidence can be combined using Dempster’s rule to give
m({Flu, Cold}) = 0.48 , m({Allergy, Flu, Cold}) = 0.32 ,
m({Flu, Cold, Pneu}) = 0.12 , m(Θ) = 0.08.
a. Write the conceptual dependency for the statements
i) “Ram ran yesterday.” ii) “Bill gave John a book.”
Ans:
i)
ii)
M.Tech 2ND SEM
2
2
USN
1 P E
PESIT Bangalore South Campus
Hosur road, 1km before Electronic City, Bengaluru -100
Department of Computer Science And Engineering
b. Explain the following primitive acts
MBUILD, PROPEL, MTRANS
Ans:
MBUILD-- Construct new information from old. e.g. decide.
PROPEL-- Application of a physical force to an object. e.g. push.
MTRANS-- Transfer of mental information. e.g. tell.
c. Write a script for robbing a bank.
Ans: Alternatives scripts are also possible too.
M.Tech 2ND SEM
3
3
USN
1 P E
PESIT Bangalore South Campus
Hosur road, 1km before Electronic City, Bengaluru -100
Department of Computer Science And Engineering
M.Tech 2ND SEM