## Check Online CS607 Artificial Intelligence Current and Past Final Term Papers 2013

Check Online CS607 Artificial Intelligence Current and Past Final Term Papers Fall 2013

2 mark Questions:
1. What is hypothesis space?
The hypothesis space used by a machine learning system is the set of all hypotheses that might possibly be
returned by it. It is typically dened by a hypothesis language, possibly in conjunction with a language bias.

2. What is machine learning?
Generally speaking, the goal of machine learning is to build computer systems that can learn from their
experience and adapt to their environments. Obviously, learning is an important aspect or component of
intelligence.

3. What is a strip?
STRIP is one of the founding languages developed particularly for planning. Let us understand planning
to a better level by seeing what a planning language can represent.

4. What is differing between height and depth of a tree?
Height and depth of a tree is equal but height and depth of a node is not equal because the height is
calculated
by traversing from leaf to the given node depth is calculated from traversal from root to the
given node.

4 questions of 3 marks
2. What do you know about Mamdanis fuzzy inference method?

3. What is the basic key of candidate elimination algorithm?
The key idea in Candidate-Elimination algorithm is to output a description of the set of all hypotheses
consistent with the training examples. This subset of all hypotheses is actually the version space with
respect to the hypothesis space H and the training examples D, because it contains all possible versions
of the target concept.

4. Predicate action?
Action is a predicate used to change states. It has three components namely, the predicate itself, the pre-
condition, and post-condition predicates. For example, the action to buy something item can be
represented as,
Action:

Pre-conditions:
at(Place) sells(Place, X)

Post-conditions/Effect:
have(X)

4 questions of 5 marks.
1. Differ between supervised and unsupervised learning? 5 marks
Unsupervised Learning
 The model is not provided with the correct results during the training.
 Can be used to cluster the input data in classes on the basis of their sta?s?cal proper?es only.
 Cluster significance and labeling.
 The labeling can be carried out even if the labels are only available for a small number of objects
representative of the desired classes.

Supervised Learning
 Training data includes both the input and the desired results.
 For some examples the correct results (targets) are known and are given in input to the model during the
learning process.
 The construction of a proper training, validation and test set (Bok) is crucial.

2. What are the linear separable line and non linear separable line?5
There is a whole class of problems which are termed as linearly separable. This name is given to them,
because if we were to represent them in the input space, we could classify them using a straight line. The
simplest examples are the logical AND or OR. We have drawn them in their input spaces, as this is a
simple 2D problem. The upper sloping line in the diagram shows the decision boundary for AND gate,
above which, the output is 1, below is 0.

CS607 - Final Term Papers Fall 2012

2 marks.
Out of different general components of and AI cycle which two components are best closed coupled.
Knowledge representation (KR) and reasoning are closely coupled components; each is intrinsically tied
to the other.

How can you elaborate the condition predicate in STRIPS.
Condition predicates are the predicates that define states. For example, a predicate that specifies that we
are at location X‟ is given as.
at(X)

Define deductive learning.
Deductive learning works on existing facts and knowledge and deduces new knowledge from the old.
This is best illustrated by giving an example. For example, assume:
A = B
B = C
Then we can deduce with much confidence that:
C = A

3 Marks
Difference between fuzzy logic and Crisp value.
The input variables in a fuzzy control system are in general mapped by sets of membership functions similar to
this, known as "fuzzy sets". The process of converting a crisp input value to a fuzzy value is called
"fuzzification".

Who does neural network resemble the human brain.
It resembles the brain in two respects:
 Knowledge is acquired by the network through a learning process (called training)
 Interneuron connection strengths known as synaptic weights are used to store the knowledge

How can you elaborate the importance and need of hypothesis space.

Write down fuzzy statement in everyday life.
Fuzziness can be found in many areas of daily life, such as in engineering, medicine, meteorology,
manufacturing and others. it is particularly frequent in all areas in which human judgment, evaluation and
decision are important. These are the areas of decision making, reasoning, and learning and so on.

5 Marks.
Write down the task for which connectionist approach is well suited.
Tasks for which connectionist approach is well suited include:
 Classification
 Fruits  Apple or orange

 Pattern Recognition
 Finger print, Face recognition

 Prediction
 Stock market analysis, weather forecast

How can you elaborate the conflict resolution strategies?
To overcome the conflict problem stated above, we may choose to use on of the following conflict resolution
strategies:
 Fire first rule in sequence (rule ordering in list). Using this strategy all the rules in the list are ordered (the ordering
imposes prioritization). When more than one rule matches, we simply fire the first in the sequence
 Assign rule priorities (rule ordering by importance). Using this approach we assign explicit priorities to rules to
allow conflict resolution.
What is the prime role of expert system designer? 2 marks
One of the prime roles of the expert system designer is to act as a knowledge engineer. As a knowledge
engineer, the designer must overcome the knowledge acquisition bottleneck and find an effective way to
get information from the expert and encode it in the knowledge base, using one of the knowledge
representation techniques we discussed in KRR.

What is inductive learning? 2 marks
Inductive learning takes examples and generalizes rather than starting with existing knowledge. For
example, having seen many cats, all of which have tails, one might conclude that all cats have tails.

Among Entropy and information gain which is the best to measure effectiveness? 2 marks
information gain

Why clustering algorithm are categorized as unsupervised? 2 marks