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Write advantages of Artificial Neural Networks. (3 Marks)

Ans:

Advantages of Artificial Neural Networks:

Excellent for pattern recognition

Excellent classifiers

Handles noisy data well

Good for generalization

Write disadvantages of Artificial Neural Networks. (5 Marks)

Ans:

1. parallel architecture:

• The power of Artificial Neural Networks is lie in their parallel architecture ,most machines

are serial (Von Neumann architecture)

2. defined rules:

• Lack of defined rules to build a neural network for a specific problem as there are too many

variables, for instance, the learning algorithm, number of neurons per layer, number of

layers, data representation etc

3. Knowledge is implicit

4. Data dependency

What is machine learning? (2 marks)

Ans:

Machine learning is the ability of knowledge based systems to improve through experience. And is

divided into three categories..

Rote learning,

Inductive learning and

deductive learning.


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what intelligence ability is better in living species than in computers. Why it is better? (2.5 + 0.5 = 3

marks)

computers are better than people are at remembering things exactly and at performing complex

numeric calculations with speed. But human brains still beat computers in a number of ways. For one,

humans can integrate information from many different variables and stimuli, and they can learn by

experience, observation and experimentation. Computers can't easily adapt to changing situations.

Sure, they can be programmed to perform outstandingly in a particular field, but they are not able to

function in multiple disciplines. Moreover, the things that make humans truly unique (emotion,

empathy, self-awareness, ambition) are beyond the capacity of computers.

what term is used to describe the relationship between input and output? (2 marks)

discuss ID3 in decision tree representaion(5marks) ID stands for interactive dichotomizer.The first step of ID3 is to find the root node. It uses a special

function GAIN, to evaluate the gain information of each attribute. For example if there are 3

instances, it will calculate the gain information for each. Whichever attribute has the maximum gain

information, becomes the root node. The rest of the attributes then fight for the next slots.

ESDLC stages(5marks)

Feasibility study Rapid prototyping Alpha system (in-house verification) Beta system (tested by users) Maintenance and evolution discuss POP algorithm(5 mrks) The partial-order planning algorithm – POP: POP(initial_state, goal, actions) returns plan Begin


Initialize plan ‘p’ with initial_state linked to goal state with two special actions, start and finish Loop until there is not unsatisfied pre-condition Find an action ‘a’ which satisfies an unachieved pre-condition of some action ‘b’ in the plan Insert ‘a’ in plan linked with ‘b’ Reorder actions to resolve any threats End In POP algorithm pre-conditions of finish action are not met. We just backtrack by adding actions that meet these unsatisfied pre-condition predicates. New unsatisfied preconditions will be generated for each newly added action. Then we try to satisfy those by using appropriate actions. we keep on doing that until there is no unsatisfied precondition. linear model,s phases any 5(3 marks) The main phases of the linear sequence are Planning Knowledge acquisition and analysis Knowledge design Code Knowledge verification System evaluation what is robotics?write the name of different active areas involved in robotics?(3 marks) Robotics: Robotics is the highly advanced and totally hyped field of today. Literally Speaking, robotics is the study of robots. Robots are complex combination of hardware and intelligence, or mechanics and brains. Active areas involved in robotics: robotics is truly a multi-disciplinary area, having active contributions from, physics, mechanics, biology, mathematics, computer science, statistics, control thory, philosophy, etc. Differentiate find s and candidate elimination algorithms (2marks)

Find-S :

Find-S is guaranteed to output the most specific hypothesis h that best fits positive training examples.


The hypothesis h returned by Find-S will also fit negative examples as long as training examples are correct.

Candidate-Elimination:

Candidate-Elimination gives Outputs a description of set of all hypotheses consistent with the training examples.

which is best techniq to go from h<?,?>to h<fi,fi>(2marks) what is knowledge aqcuisition?(2 marks) how we can represent fuzzy sets?(2 marks) what is computer vision(2 marks) mcqs latest humary hi quizez mian say thay zada .kuch mid term main say b thay....\

53 total 40 mcqs ( all r frm past papers +8 new) 13 descrptive 5 questions of 2 marks 1)truth table of And operator in fuzzy system 2)what is inductive learning 3)what is hypothesis space 4)why we need validation while training 5) Consider a situation in which we want to find relationship between input and given data by using genetic algorithm. which application field of GA this problems belongs to. 5 questions of 3 marks 1)what is ID3 2) Advantages of ANNs 3)application area of computer vision 4)law of excluded middle 5) situation about BFS 3 questions of 5 marks 1) steps of unsupervised learning methodology 3) how to apply implication method In implication method we use the degree of support for the entire rule to shape

the output fuzzy set.

The consequent of a fuzzy rule assigns an entire fuzzy set to the output.

This fuzzy set is represented by a membership function that is chosen to indicate the qualities of the

consequent.


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If the antecedent is only partially true, i.e., is assigned a value less than 1, then the output

fuzzy set is truncated according to the implication method.

One rule is usually not enough so 2-3 rule implemented by it. The output of each rule is a fuzzy set.

The output fuzzy sets for each rule are then aggregated into a single output fuzzy set. Finally the

resulting set is defuzzified, or resolved to a single number

2) find the entropy of S lec #38 first example (5Mrks)

3) how working memory is related to the Expert System? (2Marks)

working memory contains the problem facts that are discovered during the session’ according to

Durkin. One session in the working memory corresponds to one consultation.

4) what is the basic task of clustering (3 marks)

The basic task of clustering is to identify and group similar individual data elements based on some

measure of similarity. So basically using clustering algorithms, classification information can be

produced from a training data

5)why we need hypothesis space (2 marks)

all problem discuss in the Ai are real world example/cases where concept space consists of larger

number of attributes.So the learner has to apply some hypothesis, which has either a search or the

language bias to reduce the size of the concept space. This reduced concept space becomes

the hypothesis space.

7) what do you know about Mandani's fuzzy inference (3Marks)

Mamdani's fuzzy inference method is the most commonly seen fuzzy

methodology. It was among the first control systems built using

fuzzy set theory. Ebrahim Mamdani proposed it in 1975 by as an attempt to

control a steam engine and boiler combination by synthesizing a set of linguistic

control rules obtained from experienced human operators.

9)what is the consequent?

consequent is the component of rule and it make the conclusion or the THEN part of the RULE.


Q2) What is MSE? 2Marks

MSE is the most common technique for measuring the total error in each iteration of the neural

network.

Q3) How does the search paths don’t remain independent in Genetic algorithm? 2 Marks

In GA ,At each step, current states of different pairs of these paths are combined to form new

paths. This way the search paths don't remain independent, instead they share information

with each other and thus try to improve the overall performance of the complete search space.

Q4) Define Vision space? 2 Marks

Q5) What is State in Strips? Give an example. 2 Marks

STRIPS is one of the founding languages developed particularly for planning.and State is a

conjunction of predicates represented in well-known form,

Example,

a state where we are at the hotel and do not have either cash or radio is represented as,

at(hotel) ∧ ¬have(cash) ∧ ¬have(radio)

Q6) Breifly explain the different phases of machine learning? 3 Marks

1. Training: a training set of examples of correct behavior is analyzed and some representation

of the newly learnt knowledge is stored. This often defines form of rules.

2. Validation: the rules are checked and, if necessary, additional training is given. Sometimes

additional test data are used, but instead of using a human to validate the rules, some other

automatic knowledge based component may be used. The role of tester is often called the critic.

3. Application: the rules are used in responding to some new situations.


Q7) How problem solution constitute the problem solving? 3 Marks

in problem solving ,this should be known that what will be out ultimate aim. i.e, what should be the

output of our procedure in order to solve the problem.

For example in the case of mouse, the ultimate aim is to reach the cheese. The state of world when

mouse will be beside the cheese and probably eating it defines the aim. This state of world is

also referred to as the Goal State or the state that represents the solution of the problem.

Q8) Write down the name of the some of the fuzzy logic applications? 3 Marks

• Automobile engine controls

• Anti-lock braking systems

• Color film developing systems

• Subway control systems

Q9) Write different knowledge Acquisition techniques. 5 Marks

Knowledge elicitation by interview

Brainstorming session with one or more experts. Try to introduce some

structure to this session by defining the problem at hand, prompting for

ideas and looking for converging lines of thought.

Electronic brainstorming

On-site observation

Documented organizational expertise, e.g. troubleshooting manuals

ü Planning phase of the linear model?

The planning phase of the linear model involves the following steps


Feasibility assessment

Resource allocation

Task phasing and scheduling

Requirements analysis

ü Explain Learning?

a relatively permanent change that occurs in behavior as a result of experience. Learning

occurs in various regimes. For example, it is possible to learn to open a lock as a

result of trial and error; possible to learn how to use a word processor as a result of following

particular instructions.

There’s no proper definition of learning but here are some as:

• "Learning denotes changes in a system that ... enables a system to do the

same task more efficiently the next time." --Herbert Simon

• "Learning is constructing or modifying representations of what is being

experienced." --Ryszard Michalski

• "Learning is making useful changes in our minds." --Marvin Minsky

ü Explain Planning?

Planning is an advanced form of problem solving which generates a sequence of operators

that guarantee the goal. Furthermore, such sequence of operators or actions is called a plan. it

is based on logic representation

ü What Is Information Gain?


Information gain is the expected reduction in entropy caused by partitioning the examples

according to its attribute. i.e, if we use the attribute with the maximum information gain as the

node, then it will classify some of the instances as positive or negative with 100% accuracy,

and this will reduce the entropy for the remaining instances.

ü 2 main functions of brain in human system?

Introspection: that is trying to catch out own thoughts as they go by.

Psychological Experiments: that concern with the study of science of

mental life.

ü Why clustering is the example of unsupervised ?

in Unsupervised learning The data have no target attribute.We want to explore the data to find some

intrinsic structures in them. Clustering is often called an unsupervised learning task as no class

values denoting an a prior grouping of the data instances are given, which is the case in supervised

learning. there is no supervision of classification in clustering algorithms for their

learning/clustering, and hence they fall under the category of unsupervised learning.

ü Application of the fuzzy operators to antecedent?

Antecedents can have multiple parts:

• If wind is mild and racquets are good then playing badminton is fun In this case all parts of

the antecedent are resolved simultaneously and resolved to a single number using logical

operators

ü Machine Learning in Expert system? 5 marks

The primary application of machine learning inexpert systems is to attempt to solve

theknowledge acquisition bottleneck. Mostly Inductive and deductive learning phases from

machine learning took part in expert system. The job of the learning algorithm is to find


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suitable rules that are correct with respect to the examples and existing knowledge.

inductive learning is based on the knowledge that if something happens a lot it is likely to be

generally true.while Deductive learning working on existing facts and knowledge and

deduces new knowledge from the old. In contrast, inductive learning uses examples

and generates hypothesis based on the similarities between them.

ü Given statement is about version space or concept space? 5marks

ü Robot features? 5 marks

The features that constitute a robot are:

Mobility

Perception

Planning

Searching

Reasoning

Dealing with uncertainty

Vision

Learning

Autonomy

Physical Intelligence

We can get optimal solution given some parameters using Genetic Algorithm.

Select correct option:


1. True

2. False

___________ reasoning is based on forming, or inducing a ‘generalization’ from a

limited set of observations.


1. Deductive

2. Abductive

3. Analogical

4. Inductive

______ is the process of deriving logical conclusions from given facts.


1. Representation

2. Execution

3. Reasoning

4. Planning

Identify the correct step used to start designe of an expert system.


1. Feasiblity study CORRECT

2. Problem recognization

3. Scope study

4. Rapid prototyping

Inductive learning is based on the knowledge that if something happens a lot it is likely to be

generally _________

1. True CORRECT

2. False

3. Ambiguous

4. None of the given


If the antecedent is only partially true, then the output fuzzy set is truncated according to the

_________ method


1. Intrinsic

2. Implication CORRECT

3. Boolean


Question # 8 of 10 ( Start time: 10:46:28 PM ) Total Marks: 1

Choose the fields in which Fuzzy inference systems have been successfully applied:


1. automatic control

2. data classification

3. decision analysis

4. All of the given CORRECT


Usually a _________ graph is chosen to represent a fuzzy set.


1. Triangular CORRECT

2. Circular

3. Conical



Fuzzy logic is actually a superset of conventional boolean logic

1. TRUE CORRECT

2. FALSE

Reasoning in fuzzy logic is just a matter of generalizing the familiar _________ logic.


1. Boolean CORRECT

2. Complex

3. Coagnitive

4. Supervised


A classical set is a container, which wholly includes or wholly excludes any given element.


1. TRUE CORRECT

2. FALSE


The degree of truth that we have been talking about is specifically driven out by a function called the

___________ function.

1. Membership CORRECT

2. Ordinary

3. Fuzzy

4. Inline

Question # 2 of 10 ( Start time: 10:27:24 AM ) Total Marks: 1

The tractable problems are further divided into structured and ________ problems

1. Non-structured

2. Complex

3. Simple

If the antecedent is only partially true, then the output fuzzy set is truncated according to

the _________ method

1. Intrinsic

2. Implication


MSE stands for

Mean Square Error

Any statement can be ??

fuzzy

CS607 papers

( Short/long Solved Questions)

These are of 2 and 3 marks.

1) What is Fuzzy logic? Ans:

Fuzzy logic is a superset of conventional (Boolean) logic that has been extended

to handle the concept of partial truth -- truth values between "completely true" and

"completely false".

2) What are the steps of Linear Sequence? Ans:

A linear sequence of steps is applied


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repeatedly in an iterative fashion to develop the ES. The main phases of the

linear sequence are

• Planning

• Knowledge acquisition and analysis

• Knowledge design

• Code

• Knowledge verification

• System evaluation

3) Write the Predicate Action? Ans:

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:

buy(X)

Pre-conditions:

at(Place) ∧ sells(Place, X)

Post-conditions/Effect:

have(X)


4) Weak AI and Strong AI with example? Ans:

AI is classified differently by two major schools of thought. One school classifies

AI as study of systems that think like humans i.e. strong AI and the other

classifies AI as study of systems that act like humans i.e. weak AI.

5) Where the machine learning is used : examples Ans:

vision, robotics, soft-computing

and clustering.

6) What is the Linear Separable line? Ans:

if we represent whole class of problems in the input space,

and we could classify them using a straight line. It is known as the Linear Separable line The simplest examples are the

logical AND or OR.

7) Genetic algorithms are Geniality use for what? Ans:

GA is naturally applied in machine learning applications. For example

one usage of genetic-fuzzy system is of ‘searching’ for an acceptable fuzzy


system that conforms to the training data. In which, fuzzy sets and rules

combined, are encoded as individuals, and GA iterations refine the individuals i.e. fuzzy system, on the basis of their

fitness evaluations.

2 Questions are of 5 marks

1)Write the Find(s) algorithm.

Ans:

Initialize h to the most specific hypothesis in H

For each positive training instance x

For each attribute constraint ai in h

If the constraint ai is satisfied by x

Then do nothing

Else

Replace ai in h by the next more general

constraint that is satisfied by x

Output hypothesis h

2)Discuss the POP Algorithms?

Ans:

POP(initial_state, goal, actions) returns plan

Begin

Initialize plan ‘p’ with initial_state linked to goal state with two

special actions, start and finish

Loop until there is not unsatisfied pre-condition


Find an action ‘a’ which satisfies an unachieved pre-condition of

some action ‘b’ in the plan

Insert ‘a’ in plan linked with ‘b’

Reorder actions to resolve any threats

End

What is planning?

Ans:

Planning is an advanced form problem solving which generates a sequence of

operators that guarantee the goal. Furthermore, such sequence of operators or

actions (commonly used in planning literature) is called a plan.

What is information gain?

Ans:

information gain, is simply the expected reduction in entropy caused by partitioning the examples according to this

attribute. That is, if we use the attribute with the maximum information gain

as the node, then it will classify some of the instances as positive or negative with

100% accuracy, and this will reduce the entropy for the remaining instances.

What is clustering ?

Ans:

Clustering is a form of unsupervised learning, in which the training data is

available but without the classification information or class labels. The task of


clustering is to identify and group similar individual data elements based on some

measure of similarity. So basically using clustering algorithms, classification

information can be ‘produced’ from a training data which has no classification

data at the first place. Naturally, there is no supervision of classification in

clustering algorithms for their learning/clustering, and hence they fall under the

category of unsupervised learning.

Define Defuzzing ?

Ans:

the aggregate of a fuzzy set encompasses a range of output values, and so must be defuzzified in order to

resolve a single output value from the set. The input for the defuzzification process is a fuzzy set (the aggregate

output fuzzy set) and the output is a single number. As much as fuzziness helps the rule

evaluation during the intermediate steps, the final desired output for each variable

is generally a single number.

Knowledge acquisition?

Ans:

This is the most important stage in the development of ES. During this stage the

knowledge engineer works with the domain expert to acquire, organize and

analyze the domain knowledge for the ES. ‘Knowledge acquisition is the

bottleneck in the construction of expert systems’ (Hayes-Roth et al.). The main

steps in this phase are

• Knowledge acquisition from expert

• Define knowledge acquisition strategy (consider various options)

• Identify concrete knowledge elements

• Group and classify knowledge. Develop hierarchical representation where


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possible.

• Identify knowledge source, i.e. expert in the domain

Machine learning in developing expert systems?

Ans:

Many AI applications are built with rich domain knowledge and hence do not

make use of machine learning. To build such expert systems, it is critical to

capture knowledge from experts. However, the fundamental problem remains

unresolved, in the sense that things that are normally implicit inside the expert's

head must be made explicit. This is not always easy as the experts may find it

hard to say what rules they use to assess a situation but they can always tell you

what factors they take into account. This is where machine learning mechanism

could help. A machine learning program can take descriptions of situations

couched in terms of these factors and then infer rules that match expert's

behavior.

Concept learning

Two question of Connectionist?

Ans:

Although ID3 spanned more of the concept space, but still there is a possibility

that the true concept is not simply a mixture of disjunctions of conjunctions, but

some more complex arrangement of attributes.

(Artificial Neural Networks) ANNs can compute more complicated functions

ranging from linear to any higher order quadratic, especially for non-Boolean

concepts. This new learning paradigm takes its roots from biology inspired


approach to learning. Its primarily a network of parallel distributed computing in

which the focus of algorithms is on training rather than explicit programming.

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

Names of clustering Algorithm?

Ans:

The famous clustering algorithms are Self-organizing maps (SOM), k-means,

linear vector quantization, Density based data analysis, etc.

Artificial Neural Networks unsupervised step?

Ans:

Given a set of examples with no labeling, group them into

sets called clusters


How is the consequent affected by the antecedent?

Ans:

The consequent specifies

that a fuzzy set be assigned to the output. The implication function then

modifies that fuzzy set to the degree specified by the antecedent. The most

common ways to modify the output fuzzy set are truncation using the min function

(where the fuzzy set is "chopped off“).

2007

Q#1 Fuzzy system in the inherence method. 15 marks

Rule 1 Newspaper is simple, but does not cover all newspaper is below average

Rule 2 Newspaper is harder to understand, but newspaper is average

Rule 3 Newspaper is simple, And cover all newspaper average is above.

Draw graph and write down steps.

Note: Don’t use Matlab and just perform on exams software text area.

Q #2 15 marks

1-Is a computer vision possible without AI techniques?

Ans:

No,computer vision is not possible without AI techniques. It is a subfield of Artificial Intelligence. The purpose of

computer vision is to study algorithms, techniques and applications that help us make machines that can

"understand" images and videos. Computer vision finds its applications in medicine, military, security and

surveillance, quality inspection, robotics, automotive industry and many other areas. Few areas of vision in which

research is benig actively conducted throughout the world are as follows:


1. The detection, segmentation, localisation, and recognition of certain objects in images (e.g., human faces) 2. Tracking an object through an image sequence 3. Object Extraction from a video sequence 4. Automated Navigation of a robot or a vehicle 5. Estimation of the three-dimensional pose of humans and their limbs 6. Medical Imaging, automated analysis of different body scans (CT Scan, Bone Scan, X-Rays) 7. Searching for digital images by their content (content-based image retrieval) 8. Registration of different views of the same scene or object .

Is here AI brain exists?

3-Intelligence increases by age? Write comments.

Ans:

Intelligence does not increase with age,but increases with ones exposure to facts around and accumulation of

information and capacity to corelate assimilate and apply the informtion acquired.As with increasing age exposure

increases, and worldly wisdom acquired it is normally said that interlligence increase with age,however a peak is

reached and the increase in intelligance is halted or somtimes with fading memory with age a down ward trend is

observd.

Q#3 10 marks

Prove E by using of PONENS, TOLENS, INTRODUCTION, ELIMINATION Methods

(AvB) (C^-D)

-E D

AvB


Prove E by using Refutation Method.

(A vE) (-D)

-E D

Q# 4 15 marks

Find S and G and Candidate Elimination Method.

D A B C YES/NO

D1 G I W 1

D2 F I W 1

D3 F J V 0

D4 G K V 1

D5 F K W 1

2006

Question No. 1

At the end of a Candidate Elimination run, the sets G and S are given as:

G = {(?, X, ?, ?)},

S = {(?, X, ?, ?)},

Answer the following by giving arguments in support of your answers.

a) Are these sets possible together?

b) Which one of the following is the correct interpretation of the state of learning?

(i) All concepts between (?, X, ?, ?) and (P, X, M, ?) inclusive, in the generalization


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hierarchy of all concepts of the particular problem.

(ii) (P, X, M, T) is the final concept.

(iii) Learning has converged to the single concept (?, X, ?, ?).

(iv) Candidate Eliminati on will not converge in learning i.e. would fail.

(v) G and S are empty.

Question No. 2

a ) How many hypothesis (concepts) are possible if we have two attributes that can take 7

values each if we are using conjunctive (AND) logic.

) How many hypothesis (concepts) are possible if we have two attributes that can take 7

values each if we are using conjunctive (AND) logic.


Question No. 3

Suppose we have the following:

1. UV

2. U^X 3. (D ^ X)Z

4. (U ^ V)Y

We have to prove Z

a) Solve the above Inference problem using the following inference rules: Modus Ponens,

Modus Tolens, And-Introduction and And-Elimination.

b) Solve the same Inference problem above using resolution refutation. Show all steps.

Question No. 4

Suppose we want to build some preliminary rules for “Robot Motion Guidance System”

Robot Motion Guidance System

Rule I If Distance of object in front is less than 15 meters

Then look right or left

Rule II

If In right direction the distance of front object is less then 50 meters

Then move left

Rule III

If In left direction the distance of front object is less then 50 meters


Then move right.

Rule IV

If In left direction the distance of front object is less then 50 meters

AND In right direction the distance of front object is less then 50 meters

AND Distance of object in front is less than 15 meters

Then move backward

Suppose we know the following facts,

i. There is a wall in front of robot at the distance of 10 meters.

ii. There are two big hurdles in left and right directions of robot at the distances of

less than 50 meters.

a) Show that the robot will stop using forward and backw ard chaining.

b) Implement this expert system using CLIPS code.

2005

a) (5 marks) You are given the following statements:

1 U

2 ¬V

3. (V→X) →Y

4. (Y∧U)→Z

Prove E using resolution refutation. Show all steps.


b) (3+3+5 marks) The family tree below represents the relationships of a family:

The dotted circles correspond to female family members, while the solid circles correspond to male family members. The arrows represent the parent-of relationship, e.g. A is a parent of F. You can see that A is the father of F while B is the mother of F.

In a predicate logic representation of a family, we have the following predicate format: Spouse (Symbol a, Symbol b) Arguments: a is the spouse of b

Mother (Symbol a, Symbol b): Arguments: a is the mother of b Gender (Symbol a, Symbol b: Female/Male): b is the gender of a


(i) What constants would you define corresponding to the tree given?

(ii) What predicates would you define for the above tree.

(iii) Give formulae using existential and universal quantifiers for the following

relationships (You can use defined predicates in subsequent formulae, e.g. you can use the predicate father in the defining the formula daughter). All the predicates take two arguments read as (arg1, arg2): arg1 is the <> of arg2

a. Father

b. Husband

c. Step-Father

d. Maternal Aunt (Khaala)

e. Maternal Grand Father (Naana)

f. Grand Child, by son

g. Paternal Aunt (Phopho)

c) (3+3+3 Marks) Define the following terms. Give examples to support your definitions:

Question2

Waist is a fuzzy variable with universe of discourse 16 - 54 inches. The membership functions of

Waist are Fat, Medium and Thin. Draw these membership functions on three separate graphs.

Q#3


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Your problem is to generate a 32-bit word containing all 1's. Formulate the solution as a GA.

(i) Clearly write your GA steps.

(ii) Identify the initial population, fitness function, mutation and crossover steps.

(iii) Can we find the solution word with only using crossover

operation? Support your answer with arguments?

Q#4

a) (5 marks) List and explain briefly the 5 steps of fuzzy inference.

b) (10 marks) Given the fuzzy system below, perform the inference graphically just like Matlab for input A=0 and B=100. Briefly explain the steps you followed and how the output value of variable Y is obtained in no more than 10 sentences. Clearly state any assumption that you take in the inference.

Q#5

Consider the graph showing a road map for distances between cities. The values on the edges are the distance between two adjacent cities. The values on the nodes are the under-estimates (heuristics) of the remaining distance. Construct a tree from the given graph and find the solution using the A* procedure considering A as the initial node, J as the destination node, and 12 as the bound value. Eliminate the bounded subtree(s) from your solutions and only show the resultant tree.


Artificial Intelligence

Cs607

All mcq data for final

Question # 1 of 10 ( Start time: 10:27:04 PM )

IF name is "Bob" AND weather is cold THEN tell Bob "Wear a coat" The above rule is an example

of:


1. Recommendation Rule

2. Directive Rule

3. Relation Rule

4. None of the given options



Identify correct statement for the given rule. IF The aptitude level of an undergraduate student

is low and The English understanding level of undergraduate student is dull THEN He is not

eligible to go abroad for higher studies.

Select correct option (1st n 2nd option are similar…u can select any one )

a. (deftamplate UnderGradStudent (slot attribute)(slot value)) (defrule

StudentStatus (UnderGradStudent (attribute “aptitude level”)(value “low”))

(UnderGradStudent (attribute “English understanding level”)(value “dull”)) =>

(printout t “He is not eligible to go abroad for higher studies”)

b. (deftamplate UnderGradStudent (slot attribute)(slot value)) (defrule


(UnderGradStudent (attribute “English understanding level”)(value “dull”)) =>

(printout t “He is not eligible to go abroad for higher studies”))

c. (deftamplate UnderGradStudent (slot attribute)(slot value)) (defrule


(UnderGradStudent (attribute “English understanding level”)(value “dull”)) <=>

(printout t “He is not eligible to go abroad for higher studies”))

d. (defrule StudentStatus (UnderGradStudent (attribute “aptitude level”)(value

“low”)) (UnderGradStudent (attribute “English understanding level”)(value

“dull”)) => (printout t “He is not eligible to go abroad for higher studies”))

Question # 3 of 10:

Which of the following is a valid example which represents a suitable antecedent in a rule?


1. IF x > 3

2. IF name is “Bob”

3. IF weather is cold

4. All of the given options


Question # 4 of 10

Expert system technique where a hypothesis is given at the beginning and the inference engine

proceeds to ask the user questions about selected facts until the hypothesis is either confirmed

or denied


1. Network Knowledge

2. Data mining

3. Backward chaining

4. Forward chaining

Question # 5 of 10

IF A THEN B This can be considered to have a similar logical meaning as the following:


1. A -> B

2. A <-> B

3. A <- B


Question # 6 of 10

In general, the antecedent of a rule compares an object with a possible value, using an

operator.


1. True

2. False


Question # 7 of 10

A rule may have more than one _________, which usually suggests that there are multiple

actions to be taken.


1. Antecedent

2. Consequent

Question # 8 of 10

Identify the correct statement to list facts numbers 1 through 10


1. clips> (facts 1 10)

2. clips> (facts 1 to 10)

3. clips> (facts 10)

4. clips> (facts 1)

A rule, which takes a set of inputs and gives advice as a result, is called

1. Recommendation Rule

2. Directive Rule

3. Relation Rule


Question # 9 of 10


In the statement “IF A THEN B”, B is called


1. Antecedent

2. Consequent

Question # 1 of 10

_________ usually takes the form of an action or a conclusion.


1. Antecedent

2. Consequent

Question # 3 of 10

Rule, which may have a priority in expert systems, is called


1. Meta rule

2. Conflict resolution rule

3. Forward chain rule


Question # 7 of 10

Expert systems are an application in what area of artificial intelligence?


1. Cognitive science

2. Computer science

3. Robotics

4. Natural interface applications



Expert system can be expressed as:


a. It provides tools for the management, delivery, tracking, and assessment of

various types of employee learning and training

b. The set of business processes, culture, and behavior required to obtain value

from investments in information systems

c. Used for finding the optimal solution for a specific problem by examining a very

large number or possible solutions for that problem

d. Intelligent technique for capturing tacit knowledge in a very specific and

limited domain of human expertise, this knowledge is converted to rules that

can be used throughout the entire organization


____________ are able to override the normal rules in expert systems.


1. Meta rules

2. Conflict resolution rules

3. Forward chain rules



IF temperature is below 0 THEN weather is cold The above rule is used to represent _______


a. Recommendations

b. Directives


c. Relations

d. None of the given options


Clips command for adding two numbers 3 and 4 is.


a. CLIPS> (+ 3 4)

b. CLIPS> (3 4 +)


Within an expert system, the ______________ contains facts about a specific subject area and

rules that express the reasoning procedures of an expert on the subject.


1. Inference engine

2. Knowledge engineer

3. Knowledge base



Reasoning in backward chaining is known as:


1. Data-driven reasoning

2. Rule-driven reasoning

3. Intelligence-driven reasoning


4. Goal-driven reasoning


In a situation where more than one conclusion can be deduced from a set of facts, to decide

which rule to be fired we use Conflict resolution.


a. True

b. False


_____________ is caused due to un-used space in fixed size blocks/ pages.


1. Internal fragmentation

2. External fragmentation

3. Paging

4. MVT

Question # 4 of 10

Addresses generated relative to part of program, not to start of physical memory are


1. Relocatable

2. Virtual

3. Symbolic

4. Physical



If the system can allocate resources to each process in some order and still avoid a deadlock

then it is said to be in ___________ state.


a. Safe

b. Un-Safe

c. Mutual

d. Starvation


A database of rules is also called a knowledge base.


a. True

b. False


IF IELTS score of Ali is 6 and CGPA of Ali is 3.7 and GRE score of Ali is 66 Then He is eligible to

take admission in any of the University abroad


(deftamplate Ali (slot attribute)(slot value)) (defrule Alichance (Ali (attribute “IELTS score”)(value “6”)) (Ali (attribute “CGPA”)(value “3.7”)) (Ali (attribute “GRE”)(value “66”)) <= (printout t “He is eligible to take admission in any of the University abroad”))

(deftamplate Ali (slot attribute)(slot value)) (defrule Alichance (Ali (attribute “IELTS score”)(value “6”)) (Ali (attribute “CGPA”)(value “3.7”)) (Ali (attribute “GRE”)(value “66”)) => (printout t “He is eligible to take admission in any of the University abroad”)

(deftamplate Ali (slot attribute)(slot value)) (defrule Alichance (Ali (attribute “IELTS score”)(value “6”)) (Ali (attribute “CGPA”)(value “3.7”)) (Ali (attribute “GRE”)(value “66”)) => (printout t “He is eligible to take admission in any of the University abroad”))

(defrule Alichance (Ali (attribute “IELTS score”)(value “6”)) (Ali (attribute “CGPA”)(value “3.7”)) (Ali (attribute “GRE”)(value “66”)) => (printout t “He is eligible to take admission in any of the University abroad”))



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In the statement “IF A THEN B”, A is called


1. Antecedent

2. Consequent


___________________ is the part of the system that controls the process of deriving

conclusions.


1. A knowledge base

2. A database of facts

3. An interpreter, or inference engine



An alternative method is the longest-matching strategy. This method involves firing the

conclusion that was derived from the _______________.


a. Longest rule

b. Shortest rule

c. Complex rule

d. Forward chain rule


In some cases, the rules provide more definite actions such as “move left” or “close door”, in

which case the rules are being used to represent _________.

1. Recommendation

2. Directives

3. Relation

4. None of the given options.

Using deduction to reach a conclusion from a set of antecedents is called:

1. Forward chaining

2. Backward chaining


Decision trees give us disjunctions of conjunctions, that is, they have the form: (A AND B)

_______ (C AND D).


1. OR

2. AND

3. XOR


Question # 3 of 10

Hypothesis space uses the ________________ of the attributes.


1. Conjunctions (AND)


2. Disjunctions (OR)

3. Negation (NOR)



Measure of the effectiveness of an attribute in classifying the training data is called.


1. Information Gain

2. Measure Gain

3. Information Goal



Which one is NOT the advantage of Neural Network

1. Excellent for pattern recognition

2. Excellent classifiers

3. Handles noisy data well



The first step of FIND-S is to initialize h to the most specific hypothesis in __________: h = < Ø ,

Ø >

1. H

2. I

3. J

4. K



A drawback of FIND-S is that, it assumes the consistency within the training set.


1. True

2. False


In all calculations involving Entropy we define _________ to be ______


1. 0 log 0, 0

2. 0 log 10, 1

3. 0 log 0, 1

4. 1 log 1, 1


The soma and the enclosed nucleus in neuron play a significant role in the processing of

incoming and outgoing data.


1. True

2. False


A concept is the representation of the __________ with respect to the given attributes.


1. Solution

2. Problem

3. Knowledge



if there are multiple part to the antecedent, apply fuzzy logic______________ and resolve the

antecedent to a single number between 0 and 1.

1. Operator

2. Rules

3. Condition

4. None of given


If the true output of a concept [c(xi)] is 1 or 0 for an instance, then the output by our hypothesis

[h(xi)] is 1 or 0 as well, respectively.


1. True

2. False


Hypothesis space uses the ________________ of the attributes.


1. Conjunctions (AND)

2. Disjunctions (OR)

3. Negation (NOR)



Which one is NOT the advantage of Neural Network

1. Excellent for pattern recognition


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2. Excellent classifiers

3. Handles noisy data well



The first step of FIND-S is to initialize h to the most specific hypothesis in __________: h = < Ø ,

Ø >

1. H

2. I

3. J

4. K


A drawback of FIND-S is that, it assumes the consistency within the training set.

1. True

2. False


In all calculations involving Entropy we define _________ to be ______

1. 0 log 0, 0

2. 0 log 10, 1

3. 0 log 0, 1

4. 1 log 1, 1

Machine learning typically follows__________phases according to FInaly.


1. Two

2. Three

3. Four

4. Five.

Mamdani’s method was among the irst __________built using fuzzy set theory

1. Control system

2. Expet system

3. Decision analysis system

4. None of given

In theoretical computer science there are two mail branches of problem.

1. Tractable and intactable

2. Intractable and induction

3. Tractable and induction

4. None of given

In candidate-elimination algorithm version spaces is represented by two sets named

1. G and S

2. G and F

3. And F

4. H and S

In Anns. MSE is known as

1. Most sequared Error

2. Mean squared error

3. Medium squared error


4. None of given

The entropy is 1 when the collection contains number of positive example ___________to/than

negative example.

1. Equal

2. Greater

3. Less

4. None of given

The multilayer perceptions are the most basic artificial neural.______

1. Network

2. Layers

3. Icon

4. None of given

Which is the advantage of the neural network

1. The power of ANNs lie in their parallel architecture

2. Less defined rules to build a neural network for the specific problem

3. Knowledge is implicit

Question # 1 of 10 ( Start time: 10:26:01 AM )

Identify the correct definition of linear model given below.

1. A linear sequence of steps is applied repeatedly in an iterative fashion to develop the

software models.

2. A non sequential sequence of steps is applied repeatedly in an iterative fashion to

develop the expert systems.

3. A non linear sequence of steps is applied repeatedly in an iterative fashion to develop

the expert systems.


4. A linear sequence of steps is applied repeatedly in an iterative fashion to develop the

Expert Systems.


The tractable problems are further divided into structured and ________ problems

1. Non-structured

2. Complex

3. Simple



Complex problems usually have well-defined steps

1. True

2. False


Fuzzy logic is a subset of conventional (Boolean) logic.

1. TRUE

2. FALSE (superset)


Many machine learning systems are classifiers

1. True

2. False



Usually a _________ graph is chosen to represent a fuzzy set.

1. Triangular

2. Circular

3. Conical



In Fuzzy Inputs we resolve all fuzzy statements in the antecedent to a degree of

membership between 0 and _____.

1

2

3

4


Identify the step involved in planning phase.

1. Knowledge acquisition from expert

2. Coding

3. Resource allocation

4. Identify concrete knowledge elements


Which statement about learning is NOT true:

1. Learning is constructing or modifying representations of what is being experienced

2. Learning denotes changes in a system that enables a system to do the same task more

efficiently the next time


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3. Learning is making useful changes in our minds.



Inductive learning is based on the knowledge that if something happens a lot it is likely to

be generally _________

1. True

2. False

3. Ambiguous



_________ logic lets us define more realistically the true functions that define real world

scenarios.

1. Fuzzy

2. Classical

3. Boolean



Identify the correct step used to start design of an expert system.

1. Feasibility study

2. Problem recognization

3. Scope study


Aggregation only occurs once for each output variable, just after the fifth and final step,

defuzzification.


1. True

2. False ………. prior


Machine learning is a prerequisite for any mature program of artificial intelligence

1. True

2. False


_________ is the process of formulating the mapping from a given input to an output using

Fuzzy logic.

1. FIZ

2. FIS….. Fuzzy inference system (FIS)

3. FOS



The degree of truth that we have been talking about is specifically driven out by a

function called the ___________ function.

1. Membership

2. Ordinary

3. Fuzzy

4. Inline


Which one is NOT the phase of machine learning:

1. Training

2. Application


3. Validation


__________ is the process by which the fuzzy sets that represents the outputs of each rule

Are combined into a single fuzzy set.

1. Aggregation

2. Fuzzification

3. Implication



Reasoning in fuzzy logic is just a matter of generalizing the familiar _________ logic.

1. Boolean

2. Complex

3. Coagnitive

4. Supervised

Identify the sets in which Membership Function is used.

1. Crisp set

2. Classical set

3. Fuzzy set

4. non of the above

The role of tester is often called the critic

1. True

2. False


If the antecedent is only partially true, then the output fuzzy set is truncated according to the

_________ method

1. Intrinsic

2. Implication

3. Boolean



General stages of ESDLC includes.

1. Spiral model

2. Linear model

3. Beta system (tested by users)

4. Design coding


A classical set is a container, which wholly includes or wholly excludes any given element.

1. TRUE

2. FALSE


Identify the statement which best defines the fuzzy sets.

1. Fuzzy sets, unlike classical sets, restrict themselves to something lying wholly in either

set A or in set not-A.

2. Fuzzy sets, like classical sets, restrict themselves to something lying wholly in either set

A or in set not-A.

3. Fuzzy sets, unlike classical sets, do not restrict themselves to something lying wholly in

either set A or in set A.


4. Fuzzy sets, unlike classical sets, do not restrict themselves to something lying wholly in

either set A or in set not-A.


_____________ learning works on existing facts and knowledge and deduces new knowledge

from the old.

1. Deductive

2. Inductive

3. Application

4. None of given


Fuzzy logic is actually a superset of conventional boolean logic

1. True

2. False


Choose the fields in which Fuzzy inference systems have been successfully applied:

1. Automatic control

2. Data classification

3. Decision analysis

4. All of the given


Inductive learning takes examples and generalizes rather than starting with __________

knowledge.

1. Existing


2. Inductive

3. Deductive

4. None of given


Identify which statement defines classical sets in a best way

1. A classical set is a container, which wholly includes but not wholly excludes any given

element.

2. A classical set is a container, which does not wholly includes or wholly excludes any

given element

3. A classical set is a container, which sometimes wholly includes or wholly excludes any

given element.

4. A classical set is a container, which wholly includes or wholly excludes any given

element.


Fuzzy inference systems (FIS) are NOT associated with a number of names.

1. True

2. False


Sequence wise main phases of Linear model used in developing expert systems are given

below.

1)Planning

2)Know ledge acquisition and analysis

3)System evaluation

4)Know ledge design

5)Code

6)Know ledge verif ication


1)Planning


3)Know ledge design

4)System evaluation

5)Code


1)Planning


3)Know ledge design

4)Code

5)System evaluation


1)Planning


3)Know ledge design

………….true


A concept is the representation of the __________ with respect to the given attributes.


1. Solution

2. Problem

3. Knowledge



Fuzzy inference systems (FIS) have multidisciplinary nature.


1. True


2. False


Identify the correct definition of linear model given below.


a. A linear sequence of steps is applied repeatedly in an iterative fashion to develop

the software models.

b. A non sequential sequence of steps is applied repeatedly in an iterative fashion

to develop the expert systems.

c. A non linear sequence of steps is applied repeatedly in an iterative fashion to

develop the expert systems.

d. A linear sequence of steps is applied repeatedly in an iterative fashion to

develop the Expert Systems.

CS607

Artificial intelligence

Mid term papers / Mcqs / quizzes…..

In GA, the random process is repeated until an individual with required _________ level is found.

Select correct option: 1. Higher 2. Lower 3. Fitness (11)

4. Logical Mutation can be as simple as just flipping a bit at random or any number of bits

Select correct option: 1. True (4)

2. False Genetic algorithm uses evolutionary techniques, based on function optimization and artificial intelligence, to develop a solution.


1. True 2. False

We can get optimal solution given some parameters using Genetic Algorithm.

1. True 2. False

___________ reasoning is based on forming, or inducing a „generalization‟ from a limited set of observations. Select correct option:

1. Deductive 2. Abductive 3. Analogical 4. Inductive

A proposition is the statement of a ________.

Select correct option: a. Equation b. Action c. Theorem d. Fact

An AI system has a ____________ component that allows the system to get information from its environment. Select correct option:

1. Planning 2. Perception

3. Learning 4. Execution

A function by which we can tell which board position is nearer to our goal is called Select correct option:

1. Alternative function 2. Recursive function 3. Best function 4. Fitness function

Semantic networks are graphs, with nodes representing ____________ and arcs representing ____________ between objects. Select correct option:

1. distance, relationships 2. objects, distance 3. relationships, distance 4. objects, relationships


In the worst case of semantic network, we may need to traverse the entire network and then discover that the requested info ________. Select correct option:

1. Does not exist 2. Exists 3. Is incorrect 4. Is correct

A statement in conjunctive normal form (CNF) consists of __________

Select correct option: a. ANDs of Ors.

b. ANDs c. Ors d. Ors of ANDs

An AI system must form a meaningful and useful _____________ of the internal information. Select correct option:

1. Representation 2. Execution 3. Learning 4. Planning

__________ is the process of deriving logical conclusions from given facts.

1. Representation 2. Execution 3. Reasoning

4. Planning What is the correct order for solving a problem using GA I. Choose the best individuals from the population for crossover II. Choose initial population III. Evaluate the fitness of each individual

1. I,II,III 2. I,III,II 3. II,I,III 4. II,III,I

Mamdani's method was among the first _____________ built using fuzzy set

theory.


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Control Systems

Expert Systems

Decision analysis Systems

None of the given

Outputs of learning are determined by the ___________


Application

Validation

Training

None of the given

aArtificial Neural Networks is a new learning paradigm which takes its roots from

_________ inspired approach to learning.

Chemistry

Physics

Biology

Mathematics

Fuzzy inference systems (FIS) have multidisciplinary nature.

True

False

In ANNs, MSE is known as

Most Squared Error

Mean Squared Error

Medium Squared Error

None of the given

__________ is the process by which the fuzzy sets that represent the outputs of

each rule are combined into a single fuzzy set.



1. Aggregation

2. Fuzzification

3. Implication


Inductive learning is based on the knowledge that if something happens a lot it is

likely to be generally _________

1. True

2. False

3. Ambiguous


The input of the aggregation process is the list of truncated output functions

returned by the _________ process for each rule.


Truncation

Implication

Aggregation

None of the given

Aggregation only occurs once for each output variable, just after the fifth and

final step, defuzzification.

1. True

2. False (coz it occurs prior)

Decision trees give us disjunctions of conjunctions, that is, they have the form:

(A AND B) _______ (C AND D).

1. OR

2. AND

3. XOR



Identify the correct step used to start design of an expert system

1. Feasibility study

2. Problem recognition (not sure)

3. Scope study


The goal of knowledge analysis is to analyze and structure the knowledge gained during the planning phase.

1. True 2. False

Fuzzy inference system is NOT associated with number of names.

1. True 2. False

Identify the statement which best defines the fuzzy sets.

1. Fuzzy sets, unlike classical sets, restrict themselves to something lying wholly in either set A or in set not- A.

2. Fuzzy sets, like classical sets, restrict themselves to something lying wholly in either set A or in set not- A.

3. Fuzzy sets, unlike classical sets, do not restrict themselves to something lying wholly in either set A or in set A.

4. Fuzzy sets, unlike classical sets, do not restrict themselves to something lying wholly in either set A or in set not- A.

Reasoning in fuzzy logic is just a matter of generalizing the familiar………..logic. 1. Boolean 2. Complex 3. Cognitive 4. Supervised.

Most of the solution spaces for problems can be represented in a ________

1. Graph

2. Table

3. Demo In Depth First Search the node with the largest value of height will be at the


___________ priority to be picked. 1. Minimum 2. Maximum 3. Zero 4. Both maximum and minimum.

Can we precisely define Artificial Intelligence?

1. Yes, we can 2. No, we can not.

Ability to tackle ambiguous and fuzzy problems demonstrate.

1. Intelligence

2. Non Intelligence behavior 3. All of the given 4. None of the given

In Adversarial search there may occur such a scenario where two opponents also called ___________ are searching for a goal.

1. Adversaries 2. Friends 3. Players 4. Intruders

Some essential components of problem solving are Problem Statement, _________, Solution Space and Operators.

1. Complex state 2. Initial state 3. Intermediate state 4. Goal state

That is own thoughts as they go by.

1. Introspection

2. Psychological expressions 3. Introspection and psychological expression 4. None of the given

Best first search is a greedy approach

1. True

2. False Procedures that search the solution space in an uninformed manner are usually costly with respect to ___________.

1. Time 2. Space


3. Time and space both 4. All of the given

We use graphs to represent problems and their solution spaces.

1. True 2. False

From discipline of _____________ we have information about the network structure of a human brain and all the theories on functionalities of different human organs.

1. Mathematics 2. Biology

3. Computer science 4. Psychology

Which of the following disciplines provides us with the theories of structure and meaning of language.

1. Linguistic 2. Philosophy 3. Biology 4. Psychology

The traveling inside a solution space requires something called as ___________.

1. Operands 2. Inner solution 3. Space solution 4. Operators.

AI actually tries to recreate the functions of the inside of the brain as opposed to simply emulating behavior

1. Weak 2. Strong 3. Weak and strong 4. None of the given

______ AI treats the brain as a black box and just emulates its functionality.

Weak

Strong

Weak and strong

None of the given Answering the Sequence Problem need

1. Intelligence

2. Ability to make plan


3. Ability to schedule 4. None of the given

Which one is not the phase of machine learning.

1. Training 2. Application 3. Validation 4. None of the given(too tricky as aal r phases of machine learning)

General stages of ESDLC includes.

1. Spiral model 2. Linear model

3. Beta system 4. Design coding

identify correct statement for the given rule,if the aptitude level of an undergraduate student is low and the english understanding of undergraduate student is dull THEN he is not eligible to go abroad for higher studies.

in fuzzy inputs we resolve all fuzzy statements in the antecedent to a degree of membership between 0 and

1

2

3

4

Identify the steps involving in planning phase.


1. Knowledge acquisition from expert 2. Coding 3. Resource allocation 4. Identify concrete knowledge element.

Identify the sets in which membership function is used.

1. Crisp set 2. Classical set 3. Fuzzy set 4. None of the above

Choose the field in which fuzzy inference system is successfully applied

1. Automatic control 2. Data classification 3. Decision analysis 4. All of the given

if the antecedent is only partially true,then the output fuzzy set is truncated according to the .........method.

1. Intrinsic 2. Implication 3. Boolean 4. None of the given.

Identify which statement defines classical set in a best way,

1. A classical set is container, which includes but not wholly excludes any given element.

2. A classical set is container, which does not wholly include or wholly excludes any given element.

3. Classical sets, either wholly include something or exclude it from the membership of a set,

The role of tester is often called the critic,

1. True

2. False By getting grips on ___________ that deal with searching techniques in graphs and trees, problem solving can be performed in an efficient manner.

1. Pseudo code

2. Algorithms

3. Charts


4. Graphs

Every graph can be converted into a tree.

1. True

2. False

In Breadth First Search the node with the largest value of height will be at the _________ priority to be picked.

1. Maximum

2. Minimum


Breadth-First Search checks all paths of a given length before moving on to any longer paths.

1. True

2. False

There are ………….search strategy.

1

2

3

4

Breadth-first search is a good idea when you are confident that the branching factor is _________

1. Extremely small

2. Small


3. Medium

4. Large

The foothill problem occurs whenever there are___________ peaks.

1. High

2. Secondary

3. Primary

4. Deep

The Plateau problem comes up when there is a mostly flat area ___________ the peaks.

1. Separating 2. Joining 3. Over 4. None of the given

Which one of the problem is more subtle, and consequently, is more frustrating:

1. Foothill problem 2. Plateau 3. Ridge 4. Box

The paths found by best-first search are likely to be __________ than those found with other methods.

1. None od the given 2. Shorter 3. Longer

In Basic Genetic Algorithm the term mutation refers to a small random ________.

1. Number 2. Change 3. Operator 4. Operand

Which of the following two components are closely coupled and each is Intrinsically tied to the other. i. Knowledge representation


ii. Reasoning iii. Execution iv. Planning

1. i & iii 2. ii & iii 3. iii & iv 4. i & ii

An ___________ is “A computer program designed to model the problem solving ability of a human expert."

1. Expert system 2. Intelligent System 3. Echo System 4. Energy System

Another expert system named __________ was developed by Digital Equipment Corporation, as a computer configuration assistant.

1. R1/XCON 2. MYCIN 3. Dendral 4. R3/XCON

An expert system may replace the expert or assist the expert.

1. True 2. False

Conventional programming focuses on _______, while ES programming focuses on ________ …

1. Solution, Problem 2. Problem, Solution 3. Problem, Expert 4. Solution, Expert

In backward chaining terminology, the hypothesis to prove is called the ________.

1. Proof 2. Goal 3. Plan


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4. None of the given “The branch of computer science that is concerned with the automation of intelligent behavior” this definition is from:

1. Luger and Stubblefield

2. Winston 3. Schalkoff 4. Bellman

Searching is a formal mechanism to explore alternatives. In Artificial Intelligence GA stands for Genetic Algorithms.

1. True

2. False ----------------- is based on forming, or inducing a „generalization‟ from a limited set of observations.

1. Inductive reasoning

2. Deductive reasoning 3. Analogical reasoning 4. Common-sense reasoning.

“A computer program designed to model the problem solving ability of a human expert” is known as -----Expert System-------- A -------Domain expert---------- is „A person who posses the skill and knowledge to solve a specific problem in a manner superior to others‟

Hill Climbing is basically a ---- depth first search ---------- with a measure of quality that is assigned to each node in the tree.

Expert system can be expressed as.

1. It provides the tools for management, tracking and assessment of various types of employee learning and training.

2. The set of business processes, culture, and behavior required to obtain value from investments in information systems.

3. Used for finding the optimal solution for a specific problem by examining a very large number of possible solutions for that problem.

4. Intelligent techniques for capturing tacit knowledge in a very specific and


limited domain of human experise, this knowledge is converted to rules that can be used throughout the entire. (not sure)

An equipment calibration expert system is an example of an expert system in the application category of..

1. Decision management 2. Maintenance / scheduling 3. Process monitoring /control 4. Diagnostic /troubleshooting

All managers of a company, no matter what level, have the same type of information needs.

1. True

2. False

If temperature is 0 then weather is cold, this rule is to represent 1. Recommendations 2. Directives 3. Relations

4. None of the given option. Expert systems are an application in what area of artificial intelligence.

1. Cognitive science 2. Computer science 3. Robotics 4. Natural inference application

in a situation where mor ethan one conclusion can be deduced from a set of facts,to decide which rule to be fired we use conflict resolution..

1. True

2. False The rule that define how conflict resolution will be used, and how other aspects of the system itself run, are called

1. meta rule 2. conflict resolution rule

3. forward chain rule 4. None of the given option.

An alternative method is the longest machine strategy. This method involves


firing the conclusion that was derived from the……… 1. Longest rule

2. Shortest rule 3. Complex rule 4. Forward chain rule

In some cases, the rules provide definite actions such as move left or close door, in which case the rules are being used to represent ……..

1. Recommendations 2. Directives 3. Relations

4. None of the given option. A database of rules is also called knowledge base.

1. True

2. False Question No: 21 ( Marks: 2 ) Give definition of Expert System. Ans: A computer program which have the problem solving ability of human. Question No: 22 ( Marks: 3 ) What is depth first search? give priority function for it. Ans: Question No: 23 ( Marks: 5 ) Write down at least 5 names for the application fields of Genetic Algorithm.


1. Automotive Design 2. Engineering Design 3. Robotics 4. Evolvable Hardware 5. Optimized Telecommunications Routing 6. Joke and Pun Generation 7. Biometric Invention 8. Trip, Traffic and Shipment Routing 9. Computer Gaming 10. Encryption and Code Breaking 11. Computer-Aided Molecular Design 12. Gene Expression Profiling 13. Optimizing Chemical Kinetic Analysis 14. Finance and Investment Strategies 15. Marketing and Merchandising Question No: 24 ( Marks: 10 ) Discuss backward chaining with the help of doctor and patient example. Question No. 12 - Write two main types of relationships in Semantic Networks. [2 marks] sol.

1. IS-A (Inheritance relation) 2. HAS (Ownership relation)

Question No. 13 Convert the following into CNF (AVB)-->(C-->D) [3 marks] Question No. 14 What is inductive reasoning? [3 marks] Question No. 15 What is backward chaining? [2 marks] Question No. 16 Write down at least 5 names for the application fields of Genetic Algorithms. [5 marks] Question No. 17 Write the steps involved in backward chaining [5 marks] My mid term paper cs607 Artificial Intelligence 04-12-2010

Q1 How does Durkin define reasoning? 2 marks Q2 What are the possible issues in forward chaining? 2 marks


Q3 How can we state the basic genetic algorithm 3marks Q4 To understand the expert system structure give some analogy from real life 3 marks Q5 What problem are faced by the semantic networks (write in detail) Q6 Write down all steps of backward chaining 5 marks MCQs hit and trail same as generate and test branching factor of DFS_________ small In genetic algorithm why the search paths don't remain independent? 2 marks Define Facts in AI. 3 marks write the command of CLISP for"Hello word" 3 marks Define Non-Monotonic reasoning 5 marks what is Explanation facility in Expert system 5 marks Pring clips command for hello world 7 times Sol. CLIPS> (loop-for-count 7 (printout t "Hello world" crlf)) Hello world Hello world Hello world Hello world Hello world Hello world Hello world FALSE

name some shall in Artificial intelligence(2)


difference in backward and forward chaining(3)

steps of backward chaining(5)

a person want to some knowledge about driving a bike what type of knowledge will be

suit able for him and y?(5)

horse riding is same as donkey riding wt u think which type of reasoning is used in this

statement and y?(3)

differentiate between perception and knowledge representation?(2)

3 Questions of 2 marks

1) Define backward chaining

2) Differentiate between Global maxima and local maxima

3) Differentiate between "Adversial Search" and "Genetic Algorithm"


1) If a person learns how to drive a truck, is it best suited to be called "Declarative

Knowledge"? Also give reasons to prove it.

2) Define the given statement of CNF

(AvB) ^(Bv~C)^(D)

note: D=(Dv~D)


1) From the three methods Mutation, Crossover or both, Which method should we use

in Genetic Algorithm if we want to get the desired solution. Also give reasons

2) What are the appropriate domains for the expert systems

(7+7 marks). Let us define the following propositions: A=x hates studying B=x wastes

time watching movies C=x attends VU lectures D=x gets a good grade in the AI course

i. Modus Ponens, Modus Tolens, AndElimination and AndIntroduction We know that if a person hates studying, they waste time watching movies. We also know that if a person does not hate studying and attends VU lectures, they will get a good grade in the AI course. There is a student who does not waste time watching movies and attends VU lectures, prove that this student will get a good grade in AI, using

ii. Resolution refutation. Also state, why do you think resolution refutation is a better strategy in practical theorem provers? (6 marks). Early man discovers fire in a

simplified chemistry world:


%s. • IF dry stones strike each other %s. • Then a spark is produced %s. • Conclusion %s. • When there is a spark, dry leaves and oxygen is present in the atmosphere, then there is fire. %s. • Encode the problem using propositions and rules. %s. • One day, early man Fred rubbed dry stones together near a bunch of dry leaves (of coarse there was oxygen, or Fred would not be alive).

•Prove using resolution refutation that Fred created a fire. (5+5+5 marks). Discuss the three strategies of problem solving briefly. Give examples to support your argument in each case.

i. Blind/UnInformed Searches ii. Informed/Heuristic Searches iii. Optimal Searches (8+2 marks). Run the Alpha Beta Procedure on the following

tree clearly indicating the pruned branches. Calculate the percentage of nodes on which the static evaluation has to be computed.

/SSSSS. HH HH AAA ZZZZZZZZZZZZZZ IIIIII AAA

SSSSSS HH HH AA AA ZZZZZZZZZZZZZ II AA AA

SS HH HH AA AA ZZZ II AA AA


SSSSS HHHHHH AAAAAAA ZZZ II AAAAAAA

SSSS HHHHHH AAAAAAAA ZZZ II AAAAAAAA



SSSSS HH HH AA AA ZZZZZZZZZZZ II AA AA

SSSS/ HH HH AA AA ZZZZZZZZZZZZ IIIIII AA AA


CS607-Artificial Intelligence Quizzes Solved By

SHINING STAR

($$)

From discipline of _____________ we have the tools and techniques to investigate the human mind and

ways to represent the resulting theories


Computer Science

Biology

Mathematics

Psychology

In theoretical computer science there are two main branches of problems

Tractable and Intractable

Intractable and Induction

Tractable and Induction

None of the given

1. For better web hunter search; we should use the search on --------basis

� Syntactic � Conceptual � Semantic � All of these

2. Which parameters should be defined for the Web Hunter---------------

� Target � Starting point � Time Constraint � Search method


� All of these

3. Web Hunter design consists of ------------------

� Initialization � Perception � Action � Effect � All of these

4. An agent is anything that can be viewed as perceiving its environment through -------------- and acting upon that environment through -----------.

� Sensors ---- Effectors

� Effectors----- Sensors

5. Search algorithms used in web hunter is--------------

� Uninformed and informed.

� Blind and uninformed � Informed and Heuristic � Any path and non-optimal � Optimal path � All of these

6. The number of directories and files is limited only by ----------------

� Storage capacity � Content availability � Both of these

7. The data structures used in web hunting contains information of -----------


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� Location Visited

� Target � File and directories

8. To quickly retrieve specific location of the web page; data structure node should use ---------------------

� URL

� Seed � Both of these

9. Network distance are measured through --------------

� ping � traceroute � Both of these

10. Which programming language is not a good choice to build web hunter-----------

� Perl � Java � COBOL

� C++ Question No: 21 (Marks: 2)

Give definition of Expert System.

Answer:

An expert system that emulates the decision making ability of a human expert. An expert system

designed to solve complex problem by reasoning and knowledge. According to Durkin. “A computer

system designed to model the problem solving ability of a human expert.”

Question No: 22 (Marks: 3)

What is depth first search? Give priority function for it.


Answer:

Depth first search is an algorithm for traversing and searching tree and graphs. Depth first search is an

uninformed search progressed by expanding the first child node of the tree and thus going deeper and

deeper until a goal is found. We will use same simple search algorithm to implementation of DFS by this

function priority 1

( )( )

P nhight n

=

Question No: 23 (Marks: 5 )

Write down at least 5 names for the application fields of Genetic Algorithm.

Answer:

1. Robotic

2. Gaming

3. Industrial Optimization

4. Genetic programming

5. Marketing startages

6. Composing music

Question No: 24 (Marks: 10)

Discuss backward chaining with the help of doctor and patient example.

Answer:

Backward chaining:

Backward chaining is an inference strategy that works backward from a hypothesis to proof. Backward

chaining is like depth first strategy. In backward chaining all the terminology the hypothesis to prove is

called goal. It is start with the list of goals. Goals may be in working memory initially so check and you

are done if found if not then search for goal in the THEN part of the rules conclusion rather than

premises this type of rule are called Goal. Premises not listed become sub goals to prove. Process

continues in a recursive fashion until a premise is found that is not supported by a rule a premise is also


called primitive if it can not be conclude by any rule when a primitive is found ask user for information

about it back track and use this information to prove sub goals and subsequently the goal.

Backward chaining example:

Rule 1:

IF : A patient has deep cough

AND : We suspect an infection

THEN : The patient has phenomena

Rule: 2

IF : The patient’s temperature is above 100

THEN : Patient has fever

Rule 3:

IF : The patient has been sick fortnight

AND : The patient has fever

THEN : We suspect an infection

Question No. 12 -

Write two main types of relationships in Semantic Networks. [2 marks]

Answer:

ISA (inheritance relationship)

HAS (Ownership relation)

Question No. 13

Convert the following into CNF

(AVB)-->(C-->D) [3 marks]

Answer:


( )A B (C D)

( ) ( )

( ) ( )

( ) ( )

A B C D

A B C D

A B D B C D

∨ → →

¬ ∨ ∨ ¬ ∨

¬ ∧¬ ∨ ¬ ∨¬

¬ ∨¬ ∨ ∨ ¬ ∨¬ ∨

Question No. 14

What is inductive reasoning? [3 marks]

Answer:

Inductive reasoning is based on forming and inducing generalization from a limit set of observation.

Inductive reasoning is also known as induction and inductive logic kind of reasoning that construct or

evaluate proposition that are abstraction of observation.

Question No. 15

What is backward chaining? [2 marks]

Answer:

Backward chaining is like a DFS. Backward chaining is inference strategy processed from hypothesis to

proof. All the hypothesis terminology using in backward chaining is called goal.Bakcward chaining

process is start with the list of goal.

PAPER#1

2 Marks question

difference between structural and shallow knowledge

issue in forward chaining

3 marks question

how to write "Hello world" in CLIPS software

In adversarial search evolution function is used to score/number the nodes? yes

or no and give reason to justify your answer

5 Marks Question


Resolution of Refutation write all steps

A person drive a truck is "Declarative Knowledge" yes or No give reason to

justify your answer

PAPER#2

23 totl q

16 mcqs

name some shall in Artificial intelligence(2)

diffrence in backward and forward chaining(3)

steps of backward chaining(5)

a person want to some knowledge about driving a bike what type of knowledge

will be suit able for him and y?(5)

horse riding is same as donky riding wt u think which type of reasoning is used

in this statemnt and y?(3)

diffrentiate between preception and knowldge representation?(2)

PAPER#3

1. Define forward chaining?

2. Difference between heuristic and structural knowledge?

3. Write Conflict resolution strategies.

4. Bike is heavy prove this is Uncertain fact on not. And give reason.

5. Difference between British museum and bound serach.

6. Define Structural knowledge.

7. How the working memory is related to expert system.


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