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Title Name Amity assignment for Artificial Intelligence and Neural Networks
University AMITY
Service Type Assignment
Course MCA
Semister Semester-VI Cource: MCA
Short Name or Subject Code Artificial Intelligence and Neural Networks
Commerce line item Type Semester-VI Cource: MCA
Product Assignment of MCA Semester-VI (AMITY)

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  Questions :-

                                                                                                                           Artificial Intelligence and Neural Networks

Section-A

1 .What is AI? Explain different definition of AI with different application of AI.

2 .What is expert system? Explain the variuos stages of expert system.

3 .What are various heuristics search techniques? Explain how they are different from the search

techniques.

4 .Describe the characteristics of Expert Systems.

5 .Write short note on the following: -

a. Robotics

b. Expert system

c. Natural Language Processing

d. Vision of Speech Processing

6 .Describe the applications of Expert Systems.

7 .When would best-first search be worse than simple breadth-first search?

 

 

Section-B

1. A problem-solving search can either forward or backward. What factors determine the choice

of direction for a particular problem?

2. What is knowledge Acquisition? What is its role in AI?

3. What do you understand by uniformed search? What are its advantages & disadvantages over

informed search? What is breadth first search better than depth first search better than depth first

and vice-versa? Explain.

         

       Section-C                                                                                                            

Question No.  1           Marks - 10

What is Artificial intelligence?          

 Options          

  1. Putting your intelligence into Computer
  2. Programming with your own intelligence
  3. Making a Machine intelligent
  4. Playing a Game

 

Question No.  2           Marks - 10

Which is the best way to go for Game playing problem?      

 options          

  1. Linear approach
  2. Heuristic approach
  3. Optimal approach
  4. Random approach

 

Question No.  3           Marks - 10

Which is not the commonly used programming language for AI?    

 Options          

  1. PROLOG
  2. Java
  3. LISP
  4. Perl

 

Question No.  4           Marks - 10

Which search method takes less memory?     

 Options          

  1. Depth-First Search
  2. Breadth-First search
  3. Both (a) and (b)
  4. Linear Search.

 

 Question No.  5           Marks - 10

A heuristic is a way of trying 

 Options          

  1. To discover something or an idea embedded in a program
  2. To search and measure how far a node in a search tree seems to be from a goal
  3. To compare two nodes in a search tree to see if one is better than the other
  4. Only (a), (b) and (c).

 

 Question No.  6           Marks - 10

What is state space?   

 Options          

  1. The whole problem
  2. Your Definition to a problem
  3. Problem you design
  4. Representing your problem with variable and parameter

 

 Question No.  7           Marks - 10

A production rule consists of 

 Options          

  1. A set of Rule
  2. A sequence of steps
  3. Arbitrary representation to problem
  4. Both (a) and (b)

 

 Question No.  8           Marks - 10

Who is called the father of AI?         

 Options          

  1. James C Gosling
  2. Dennis Ritchie
  3. Alan Turing
  4. Isaac Newton

 

Question No.  9           Marks - 10

In AI ………………………. is a combination of data structures and interpretive procedures.      

 Options          

  1. Knowledge
  2. Meta-knowledge
  3. Artificial Knowledge
  4. Performance

 

 Question No.  10         Marks - 10

The …………………… approach uses the knowledge of mathematics and engineering.   

 Options          

  1. rationalist
  2. Top-down
  3. bottom-up
  4. push-pop approach

 

  Question No.  11        Marks - 10

We also use knowledge about what we know, called ……………………..

 Options          

  1. Meta-Knowledge
  2. Performance Knowledge
  3. Standard knowledge
  4. Specific knowledge

 

Question No.  12         Marks - 10

The Artificial Intelligence is concerned with designing intelligent computer systems that exhibit intelligent characteristics expressed by …………………..        

 Options          

  1. Functional behaviour
  2. Human behaviour
  3. Human brain
  4. Statistical analysis

 

Question No.  13         Marks - 10

State whether the following true or false. i) AI is used in diverse fields like space exploration, robotics. ii) AI is used for military purpose    

 Options          

  1. i-True, ii-False
  2. i-True, ii-True
  3. i-False, ii-False
  4. i-False, ii-True

 

 Question No.  14         Marks - 10

The goals of AI systems can be described in terms of cognitive tasks like   

 Options          

  1. Recognizing objects
  2. Answering questions
  3. Manipulating robotic devices
  4. All of the above

 

 Question No.  15         Marks - 10

………………….. is computerized advice-giver, that is capable of reasoning but which is usually confined to a rather narrow field of knowledge.     

 Options          

  1. Expert system
  2. Knowledge system
  3. Common system
  4. Communication system

 

Question No.  16         Marks - 10

One method of programming a computer to exhibit human intelligence is called modeling on____________.      

 Options          

  1. simulation
  2. cognition
  3. more intelligence.
  4. programming

 

 Question No.  17         Marks - 10

Shaping teaching techniques to fit the learning patterns of individual students in the goal of _______________. 

 Options          

  1. Automatic programming.
  2. Decision support.
  3. Intelligent computer assisted instruction.
  4. Expert systems.

 

 Question No.  18         Marks - 10

What was originally called the imitation game by its creators?         

 Options          

  1. The Turing test.
  2. LISP
  3. Cyber entices.
  4. The logic theorist.

 

 Question No.  19         Marks - 10

The term artificial intelligence was coined by _____________.       

 Options          

  1. John MC Carthy.
  2. Marvin minsky.
  3. Natheniel Rochester.
  4. Claude Shannon.

 

Question No.  20         Marks - 10

Human brain has capacity to ____________.

 Options          

  1. Programe
  2. learn
  3. Learn and program.
  4. read

 

 Question No.  21         Marks - 10

An expert system differs from a database program if only an expert system__________.  

 Options          

  1. contains declarative knowledge.
  2. contains procedural knowledge.
  3. features the ret rival of sorted information.
  4. excepts users to draw their own conclusions.

 

 Question No.  22         Marks - 10

Which of the following component is an expert system?      

 Options          

  1. Inference engine.
  2. Knowledge base.
  3. User interface.
  4. GUI

 

Question No.  23         Marks - 10

In rule based system procedural domain knowledge is the form of_____________.           

 Options          

  1. production rules.
  2. rule interpreters.
  3. control rules.
  4. meta rules.

 

 Question No.  24         Marks - 10

A________________is an integral part of developing an expert system.     

 Options          

  1. cognitive scientist.
  2. domain expert.
  3. knowledge engineer.
  4. software engineer.

 

 Question No.  25         Marks - 10

A process that is repeated, evaluated, and refined is called___________.   

 Options     

1.descriptive

2.diagnostic

3.interpretive

4.interative  

 

 Question No.  26         Marks - 10

A problem is first connected to the proposed solution during the_____________ stage.    

 Options      

1.identification

2.conceptualization   

3.formalization

4.implementation

 

Question No.  27         Marks - 10

Which of the following is the heuristic evaluation function?

 Options          

  1. hill climbing search.
  2. breadth first
  3. depth first
  4. shortest path

 

Question No.  28         Marks - 10

Which is one of the brute force search technique.     

 Options          

  1. depth first search
  2. heuristic search
  3. best first search
  4. beam search

 

 Question No.  29         Marks - 10

The speed of present day computers is expressed in the terms of ___________.     

 Options          

  1. million Instructions per hour.
  2. million Instructions per second.
  3. million Instructions per day.
  4. million Instructions per minute.

 

 Question No.  30         Marks - 10

The most common methods of problem representation in AI are_____________.   

 Options          

  1. state space representation and problem.
  2. state space representation and representation.
  3. state space representation problem representation.
  4. state space representation and problem representation.

 

Question No.  31         Marks - 10

Basically the knowledge of an expert occupies three distinctive layers____________.       

 Options          

  1. factual knowledge, heuristic knowledge .
  2. factual knowledge, meta knowledge.
  3. factual knowledge, heuristic knowledge, meta knowledge.
  4. heuristic knowledge, meta knowledge.

 

 Question No.  32         Marks - 10

LISP MACHINES also known as____________.    

 Options          

  1. AI workstations.
  2. super minicomputers.
  3. time sharing terminals.
  4. client machines.

 

 Question No.  33         Marks - 10

Which of the following is an advantage of using an expert system development tool?       

 Options          

  1. Knowledge Engineering.
  2. Imposed Structure.
  3. Rapid Prototyping.
  4. protocol

 

 Question No.  34         Marks - 10

Which search technique imposes a fixed depth limit on a depth-first search.           

 Options          

  1. Depth first search
  2. Depth limited search.
  3. Breadth first search.
  4. Breadth limited search

 

 Question No.  35         Marks - 10

The search which proceeds immediately to the deepest level of the search tree is _____________.           

 Options          

  1. uniformed Search.
  2. . breadth first search.
  3. . depth first search.
  4. uniform cost.

 

Question No.  36         Marks - 10

The simple strategy in which the root node is expanded first then all the successors and so on .this is called _____.           

 Options          

  1. informed search.
  2. breadth first search.
  3. uniform cost search.
  4. linear search.

 

 Question No.  37         Marks - 10

The problem which aims to find the shortest tour in which each city must be visited exactly once is ________.   

 Options          

  1. robot navigation.
  2. traveling salesperson problem.
  3. nternet searching.
  4. search tree

 

  Question No.  38         Marks - 10

A problem which is intended to illustrate or exercise various problems solving methods which give a concise exact description?    

 Options          

  1. Real-world problem.
  2. Toy problem
  3. 8-puzzle problem.
  4. 8-queen problem.

 

 Question No.  39         Marks - 10

Which will provide patterns for arguments structures that always yielded correct conclusion when given premises is correct?          

 Options          

  1. logic
  2. syllogism
  3. philosophy
  4. Rational agent

 

Question No.  40         Marks - 10

The last component of the learning agent is __________.    

 Options          

  1. agent generator
  2. program generator.
  3. problem generator.
  4. output generator
  Answers :-

                                                                  sz/2017/1

                                                                                                                                        Artificial Intelligence and Neural Networks

Section-A

1 .What is AI? Explain different definition of AI with different application of AI.      

Answer:

Definition 1:

Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans. Some of the activities computers with artificial intelligence are designed for include:

  • Speech recognition
  • Learning
  • Planning
  • Problem solving

Definition 2:

Artificial intelligence is a branch of computer science that aims to create intelligent machines. It has become an essential part of the technology industry.

Research associated with artificial intelligence is highly technical and specialized. The core problems of artificial intelligence include programming computers for certain traits such as:

  • Knowledge
  • Reasoning
  • Problem solving
  • Perception
  • Learning
  • Planning
  • Ability to manipulate and move objects

Applications of AI

Game playing

You can buy machines that can play master level chess for a few hundred dollars. There is some AI in them, but they play well against people mainly through brute force computation--looking at hundreds of thousands of positions. To beat a world champion by brute force and known reliable heuristics requires being able to look at 200 million positions per second.

Speech recognition

In the 1990s, computer speech recognition reached a practical level for limited purposes. Thus United Airlines has replaced its keyboard tree for flight information by a system using speech recognition of flight numbers and city names. It is quite convenient. On the the other hand, while it is possible to instruct some computers using speech, most users have gone back to the keyboard and the mouse as still more convenient.

Understanding natural language

Just getting a sequence of words into a computer is not enough. Parsing sentences is not enough either. The computer has to be provided with an understanding of the domain the text is about, and this is presently possible only for very limited domains.

Computer vision

The world is composed of three-dimensional objects, but the inputs to the human eye and computers´ TV cameras are two dimensional. Some useful programs can work solely in two dimensions, but full computer vision requires partial three-dimensional information that is not just a set of two-dimensional views. At present there are only limited ways of representing three-dimensional information directly, and they are not as good as what humans evidently use.

Expert systems

A ``knowledge engineer´´ interviews experts in a certain domain and tries to embody their knowledge in a computer program for carrying out some task. How well this works depends on whether the intellectual mechanisms required for the task are within the present state of AI. When this turned out not to be so, there were many disappointing results. One of the first expert systems was MYCIN in 1974, which diagnosed bacterial infections of the blood and suggested treatments. It did better than medical students or practicing doctors, provided its limitations were observed. Namely, its ontology included bacteria, symptoms, and treatments and did not include patients, doctors, hospitals, death, recovery, and events occurring in time. Its interactions depended on a single patient being considered. Since the experts consulted by the knowledge engineers knew about patients, doctors, death, recovery, etc., it is clear that the knowledge engineers forced what the experts told them into a predetermined framework. In the present state of AI, this has to be true. The usefulness of current expert systems depends on their users having common sense.

Heuristic classification

One of the most feasible kinds of expert system given the present knowledge of AI is to put some information in one of a fixed set of categories using several sources of information. An example is advising whether to accept a proposed credit card purchase. Information is available about the owner of the credit card, his record of payment and also about the item he is buying and about the establishment from which he is buying it (e.g., about whether there have been previous credit card frauds at this establishment).

2 .What is expert system? Explain the various stages of Expert System.

Answer:

Expert Systems are computer programs that are derived from a branch of computer science research called Artificial Intelligence (AI). AI´s scientific goal is to understand intelligence by building computer programs that exhibit intelligent behavior. It is concerned with the concepts and methods of symbolic inference, or reasoning, by a computer, and how the knowledge used to make those inferences will be represented inside the machine.

Of course, the term intelligence covers many cognitive skills, including the ability to solve problems, learn, and understand language; AI addresses all of those. But most progress to date in AI has been made in the area of problem solving -- concepts and methods for building programs that reason about problems rather than calculate a solution.

AI programs that achieve expert-level competence in solving problems in task areas by bringing to bear a body of knowledge about specific tasks are called knowledge-based or expert systems. Often, the term expert systems is reserved for programs whose knowledge base contains the knowledge used by human experts, in contrast to knowledge gathered from textbooks or non-experts. More often than not, the two terms, expert systems (ES) and knowledge-based systems (KBS), are used synonymously. Taken together, they represent the most widespread type of AI application. The area of human intellectual endeavor to be captured in an expert system is called the task domain

Various stages of Expert System

Step 1:Select a domain and a particular task

  1. a) Choose a task that an expert can do well.
  2. b) The performance of the task should be related to both breadth and depth of knowledge.
  3. c) The facts and rules should be stable.

Step 2: Select the expert system shell for implementation

  1. a) Choose the type of inference control required.
  2. b) Choose the type of pattern-matching capability required.
  3. c) Decide whether certainty factors are necessary
  4. d) Start building a prototype system

Step 3: Acquire initial knowledge about the domain and the task

  1. a) Identify the knowledge experts
  2. b) Select particular problems associated with each task
  3. c) Obtain, record and cross-check factual knowledge from both reference material and experts
  4. d) Obtain and record task-related rules from the experts and confirm them as far as possible
  5. e) Prepare a set of test cases

Step 4: Encode the knowledge using the appropriate representation

  1. a) Factual knowledge
  2. b) Inference knowledge
  3. c) Control knowledge

Step 5: Execute and test the knowledge

  1. a) Evaluate the test cases
  2. b) Be alert for problems with consistency and completeness

Step 6: Refine the current knowledge and acquire additional knowledge

  1. a) Revise the rules as necessary
  2. b) Modify any facts that need revision
  3. c) Augment the system with information on additional domain tasks and test again
  4. d) Repeat as often as necessary

Step 7: Complete any necessary interface code

  1. a) Demonstrate the system
  2. b) Make the system user-friendly

Step 8: Document the expert system

  1. a) Provide on-line and hard-copy documentation as necessary
  2. b) Document the consultation portion especially well
  3. c) Document the knowledge portion to the degree necessary

3 .What are various heuristics search techniques? Explain how they are different from the search techniques.    

Answer:

All of the search methods in the preceding section are uninformed in that they did not take into account the goal. They do not use any information about where they are trying to get to unless they happen to stumble on a goal. One form of heuristic information about which nodes seem the most promising is a heuristic function h (n), which takes a node n and returns a non-negative real number that is an estimate of the path cost from node n to a goal node. The function h (n) is an underestimate if h(n) is less than or equal to the actual cost of a lowest-cost path from node n to a goal.

The heuristic function is a way to inform the search about the direction to a goal. It provides an informed way to guess which neighbor of a node will lead to a goal.

There is nothing magical about a heuristic function. It must use only information that can be readily obtained about a node. Typically a trade-off exists between the amount of work it takes to derive a heuristic value for a node and how accurately the heuristic value of a node measures the actual path cost from the node to a goal.

A standard way to derive a heuristic function is to solve a simpler problem and to use the actual cost in the simplified problem as the heuristic function of the original problem.

Example: For the graph of Figure 3.2, the straight-line distance in the world between the node and the goal position can be used as the heuristic function. The examples that follow assume the following heuristic function:

h(mail) =          26        h(ts)     =          23        h(o103)            =          21

h(o109)            =          24        h(o111)            =          27        h(o119)            =          11

h(o123)            =          4          h(o125)            =          6          h(r123) =          0

h(b1)    =          13        h(b2)    =          15        h(b3)    =          17

h(b4)    =          18        h(c1)    =          6          h(c2)    =          10

h(c3)    =          12        h(storage)        =          12

This h function is an underestimate because the h value is less than or equal to the exact cost of a lowest-cost path from the node to a goal. It is the exact cost for node o123. It is very much an underestimate for node b1, which seems to be close, but there is only a long route to the goal. It is very misleading for c1, which also seems close to the goal, but no path exists from that node to the goal.

Heuristic search algorithms have knowledge of where the goal or finish of the graph. For example, in a maze, they would know which path leads in the direction of the goal. Blind search algorithms have no knowledge of where the goal is, and wander "blindly" through the graph.

Blind search techniques include Breadth-first, Depth-first search, etc.

Heuristic search techniques include Best-first, A*, etc.

4 .Describe the characteristics of Expert Systems.

Answer:

CHARACTERISTICS OF EXPERT SYSTEMS

High performance: They should perform at the level of a human expert.

Adequate response time: They should have the ability to respond in a reasonable amount of time. Time is crucial especially for real time systems.

Reliability: They must be reliable and should not crash.

Understandable: They should not be a black box instead it should be able explain the steps of the reasoning process. It should justify its conclusions in the same way a human expert explains why he arrived at particular conclusion.

Capabilities of the Expert Systems

  1. Capturing of the expertise.
  2. Codifying the expertise.
  3. Duplicating the expertise.
  4. Transferring the expertise.
  5. Saving the human effort’s time.
  6. Saving on the maintenance.
  7. Up – dating the knowledge base on the regular basis.

Some more Characteristics of the Expert Systems

  1. Should be able to display the intelligent behavior.
  2. Should be able to explain the reasoning.
  3. Should be able to draw the conclusions from the relationships that are very complex in the nature.
  4. Should be able to provide the much needed portable knowledge.
  5. Should be able to deal with the certainty.
  6. Is not used or tested much due to the reason which says that it is difficult to use.
  7. Does not possess the ability to deal with the mixed knowledge.
  8. Cannot refine own knowledge base.
  9. Are very much difficult to maintain.
  10. Limited to the narrow problems.

5 .Write short note on the following: -

  1. Robotics

Answer:

The dictionary meaning of robot is a mechanical man or a more than humanly efficient automation. It is an automatic apparatus or device that performs functions ascribed to human beings or operates with what appears to be almost human intelligence. But this defamation does not give a human shape to the robot. The robot does the work of a human being.

The Robot Institute of America has given a very interesting definition on an Industrial robot. Industrial robots are usually used at industries. "An Industrial robot is a reprogrammable, multifunctional manipulator designed to move materials, parts, tools or special devices through variable programmed motions for the performance of a variety of tasks".

So, a robot is capable of being reprogrammed. This characteristic distinguishes it from a fixed automation. A fixed automation is designed to do one and only one specific task. If the specification of task changes even slightly the fixed automation becomes incapable of performing the task. It is designed to perform according to one fixed specification.

Applications of Robots:

Though robots are used widely in industries but they are also popular in other fields like agriculture, nuclear energy, firefighting, mining, undersea exploration, space exploration, medical applications etc.

(1) Industries:

Use of robots in industries are very common because they can offer reduced cost of production, increase productivity, improve production quality, and can operate hazardous and hostile environment. Robots, at industries are classified according to the type of work they perform like Pick- and place robots, point-to-point robots, assembly robots etc.

(2) Agriculture:

In agriculture, robots are now successfully utilized for fruit picking, potato digging etc.

(3) Nuclear energy:

Handling of radioactive substances in a nuclear power station is an important work done by robots. This relieves the human beings from doing such hazardous tasks.

(4) Firefighting and mining:

Robots can perform hazardous works like firefighting to extinguish the fire and in mining operations.

  1. Expert system

Answer:

Expert Systems are computer programs that are derived from a branch of computer science research called Artificial Intelligence (AI). AI´s scientific goal is to understand intelligence by building computer programs that exhibit intelligent behavior. It is concerned with the concepts and methods of symbolic inference, or reasoning, by a computer, and how the knowledge used to make those inferences will be represented inside the machine.

Of course, the term intelligence covers many cognitive skills, including the ability to solve problems, learn, and understand language; AI addresses all of those. But most progress to date in AI has been made in the area of problem solving -- concepts and methods for building programs that reason about problems rather than calculate a solution.

AI programs that achieve expert-level competence in solving problems in task areas by bringing to bear a body of knowledge about specific tasks are called knowledge-based or expert systems. Often, the term expert systems is reserved for programs whose knowledge base contains the knowledge used by human experts, in contrast to knowledge gathered from textbooks or non-experts. More often than not, the two terms, expert systems (ES) and knowledge-based systems (KBS), are used synonymously. Taken together, they represent the most widespread type of AI application. The area of human intellectual endeavor to be captured in an expert system is called the task domain

  1. Natural Language Processing

Answer:

The development of NLP applications is challenging because computers traditionally require humans to “speak” to them in a programming language that is precise, unambiguous and highly structured or, perhaps through a limited number of clearly-enunciated voice commands. Human speech, however, is not always precise -- it is often ambiguous and the linguistic structure can depend on many complex variables, including slang, regional dialects and social context.

Current approaches to NLP are based on machine learning, a type of artificial intelligence that examines and uses patterns in data to improve a program´s own understanding. Most of the research being done on natural language processing revolves around search, especially enterprise search. 

Common NLP tasks in software programs today include:

  • Sentence segmentation, part-of-speech tagging and parsing.
  • Deep analytics.
  • Named entity extraction.
  • Co-reference resolution.

The advantage of natural language processing can be seen when considering the following two statements: "Cloud computing insurance should be part of every service level agreement" and "A good SLA ensures an easier night´s sleep -- even in the cloud." If you use national language processing for search, the program will recognize that cloud computing is an entity, that cloud is an abbreviated form of cloud computing and that SLA is an industry acronym for service level agreement.

  1. Vision of Speech Processing

Answer:

The focus of natural language processing is to enable computers to communicate interactively with English words and sentences that are typed on paper or displayed on a screen. However, the primary interactive method of communication used by humans is not reading and writing; it is speech. The goal of speech processing research is to allow computers to understand human speech so that they can hear our voices and recognize the words we are speaking. Speech recognition research seeks to advance the goal of natural language processing by simplifying the process of interactive communication between people and computers. It is a simple task to attach a camera to computer so that the computer can receive visual images. It has proven to be a far more difficult task, however, to interpret those images so that the computer can understand exactly what it is seeing. People generally use vision as their primary means of sensing their environment; we generally see more than we hear, feel, smell or taste. The goal of computer vision research is to give computers this same powerful facility for understanding their surroundings. Currently, one of the primary uses of computer vision is in the area of robotics

6 .Describe the applications of Expert Systems.    

Answer:

THE APPLICATIONS OF EXPERT SYSTEMS

The spectrum of applications of expert systems technology to industrial and commercial problems is so wide as to defy easy characterization. The applications find their way into most areas of knowledge work. They are as varied as helping salespersons sell modular factory-built homes to helping NASA plan the maintenance of a space shuttle in preparation for its next flight.

Applications tend to cluster into seven major classes.

Diagnosis and Troubleshooting of Devices and Systems of All Kinds

This class comprises systems that deduce faults and suggest corrective actions for a malfunctioning device or process. Medical diagnosis was one of the first knowledge areas to which ES technology was applied (for example, see Shortliffe 1976), but diagnosis of engineered systems quickly surpassed medical diagnosis. There are probably more diagnostic applications of ES than any other type. The diagnostic problem can be stated in the abstract as: given the evidence presenting itself, what is the underlying problem/reason/cause?

Planning and Scheduling

Systems that fall into this class analyze a set of one or more potentially complex and interacting goals in order to determine a set of actions to achieve those goals, and/or provide a detailed temporal ordering of those actions, taking into account personnel, materiel, and other constraints. This class has great commercial potential, which has been recognized. Examples involve airline scheduling of flights, personnel, and gates; manufacturing job-shop scheduling; and manufacturing process planning.

Configuration of Manufactured Objects from Subassemblies

Configuration, whereby a solution to a problem is synthesized from a given set of elements related by a set of constraints, is historically one of the most important of expert system applications. Configuration applications were pioneered by computer companies as a means of facilitating the manufacture of semi-custom minicomputers (McDermott 1981). The technique has found its way into use in many different industries, for example, modular home building, manufacturing, and other problems involving complex engineering design and manufacturing.

Financial Decision Making

The financial services industry has been a vigorous user of expert system techniques. Advisory programs have been created to assist bankers in determining whether to make loans to businesses and individuals. Insurance companies have used expert systems to assess the risk presented by the customer and to determine a price for the insurance. A typical application in the financial markets is in foreign exchange trading.

Knowledge Publishing

This is a relatively new, but also potentially explosive area. The primary function of the expert system is to deliver knowledge that is relevant to the user´s problem, in the context of the user´s problem. The two most widely distributed expert systems in the world are in this category. The first is an advisor which counsels a user on appropriate grammatical usage in a text. The second is a tax advisor that accompanies a tax preparation program and advises the user on tax strategy, tactics, and individual tax policy.

Process Monitoring and Control

Systems falling in this class analyze real-time data from physical devices with the goal of noticing anomalies, predicting trends, and controlling for both optimality and failure correction. Examples of real-time systems that actively monitor processes can be found in the steel making and oil refining industries.

Design and Manufacturing

These systems assist in the design of physical devices and processes, ranging from high-level conceptual design of abstract entities all the way to factory floor configuration of manufacturing processes.

7 .When would best-first search be worse than simple breadth-first search?

Answer:

Best first search is different from BFS and DFS by that that it uses problem specific information to choose which node of the search tree to expand next. Best first search is informed search and DFS and BFS are uninformed searches.

In order to use infored search algorithm you need to represent the knowledge of the problem as heuristic function.

Best first search is sometimes another name for Greedy Best First Search, but it may also mean class of search algorithms, that chose to expand the most promising node based on an evaluation function(not necessary the same as the heuristics) such as Greedy Best First Search, A* and others.

If you meen Greedy Best First Search:

  • it is complete (finds a solution in finite graphs) like BFS and DFS
  • it is not optimal(to find the least cost solution) as DFS, but BFS is optimal when the cost of each arc is the same
  • In the worst case its time and space complexity is O (), where b is the branching factor and n is the maximal depth. For BFS time and space complexity is O (), where m is the depth of the shallowest goal. Greedy best-first search is in most cases better than BFS- it depends on the heuristic function and the structure of the problem. If the heuristic function is not good enough it can mislead the algorithm to expand nodes that look promising, but are far from the goal. Here is one simple example

Let all arcs have the same cost, S - start node, G - goal node and h-heuristic function

Here Greedy best-first search will expand: S, B, C, D, and G

And BFS will only expand: S, A, B, G

Section-B

  1. A problem-solving search can either forward or backward. What factors determine the choice of direction for a particular problem?

Answer:

The size of the search space of the generic search algorithm on finite uniform graphs is bk, where b is the branching factor and k is the path length. Anything that can be done to reduce k or b can potentially give great savings. The abstract definition of the graph-searching method of problem solving is symmetric in the sense that the algorithm can either begin with a start node and search forward for a goal node or begin with a goal node and search backward for a start node in the inverse graph. Note that in many applications the goal is determined implicitly by a Boolean function that returns true when a goal is found, and not explicitly as a set of nodes, so backward search may not be possible.

For those cases where the goal nodes are explicit, it may be more efficient to search in one direction than in the other. The size of the search space is exponential in the branching factor. It is typically the case that forward and backward searches have different branching factors. A general principle is to search forward or backward, depending on which has the smaller branching factor.

Whether you use forward or backwards reasoning to solve a problem depends on the properties of your rule set and initial facts. Sometimes, if you have some particular goal (to test some hypothesis), then backward chaining will be much more efficient, as you avoid drawing conclusions from irrelevant facts. However, sometimes backward chaining can be very wasteful - there may be many possible ways of trying to prove something, and you may have to try almost all of them before you find one that works. Forward chaining may be better if you have lots of things you want to prove (or if you just want to find out in general what new facts are true); when you have a small set of initial facts; and when there tend to be lots of different rules which allow you to draw the same conclusion. Backward chaining may be better if you are trying to prove a single fact, given a large set of initial facts, and where, if you used forward chaining, lots of rules would be eligible to fire in any cycle.

  1. What is knowledge Acquisition? What is its role in AI?

Answer:

Knowledge acquisition is the process used to define the rules and ontologies required for a knowledge-based system. The phrase was first used in conjunction with expert systems to describe the initial tasks associated with developing an expert system, namely finding and interviewing domain experts and capturing their knowledge via rules, objects, and frame-based ontologies.

Expert systems were one of the first successful applications of artificial intelligence technology to real world business problems. Researchers at Stanford and other AI laboratories worked with doctors and other highly skilled experts to develop systems that could automate complex tasks such as medical diagnosis. Until this point computers had mostly been used to automate highly data intensive tasks but not for complex reasoning. Technologies such as inference engines allowed developers for the first time to tackle more complex problems.

As expert systems scaled up from demonstration prototypes to industrial strength applications it was soon realized that the acquisition of domain expert knowledge was one of if not the most critical task in the knowledge engineering process. This knowledge acquisition process became an intense area of research on its own.

One approach to knowledge acquisition investigated was to use natural language parsing and generation to facilitate knowledge acquisition. Natural language parsing could be performed on manuals and other expert documents and an initial first pass at the rules and objects could be developed automatically. Text generation was also extremely useful in generating explanations for system behavior. This greatly facilitated the development and maintenance of expert systems.

  1. What do you understand by uniformed search? What are its advantages & disadvantages over informed search? What is breadth first search better than depth first search better than depth first and vice-versa? Explain.

Answer:

A problem determines the graph and the goal but not which path to select from the frontier. This is the job of a search strategy. A search strategy specifies which paths are selected from the frontier. Different strategies are obtained by modifying how the selection of paths in the frontier is implemented.

This section presents three uninformed search strategies that do not take into account the location of the goal. Intuitively, these algorithms ignore where they are going until they find a goal and report success.

Advantages:-

This uninformed search strategy can find the goal node where ever it is located in the search tree since it traverses the each node in a breadth-first manner until the goal node is reached. So we call this search strategy to be ‘Complete’ since it always guarantees that it finds the goal node at some level.

This finds the goal node is an optimal manner if each of the costs of traversing one node to the other is similar for the entire search tree.

Breadth-first Search (BFS)

In this type of search, the main idea is that the root node is expanded first and then all the successor states of the root node are expanded afterwards. In the next step the rest of the successor states of the states traversed first would be explored. This activity is continued until the goal state for that specific problem is reached. So, at each state, it is checked whether it is the goal state or not. If we explain the breadth-first search in more specific terms, it requires all the nodes at a given depth of the search tree/graph to be expanded before going to the next depth level of the tree. Following search tree shows how breadth-first traversal works.

Figure 1 – Breadth-first traversal on the state space denoted by a search tree. The root node is ‘A’ and the goal node is ‘F’.

The breadth-first traversal searches the tree in this order as depicted by the red arrows.

A->B->C->D->E->F

It is also important to identify the pros and cons of BFS strategy.

Depth-first Search (DFS)

In the depth-first search (DFS) strategy, there is a clear distinction from the BFS. This always expands the deepest node in the search tree and goes along the depth of each node rather than across the breadth. In figure 2, we have shown how DFS would work on the same search tree as used in illustrating the behaviour of BFS.

Figure 2 – Depth-first traversal on the state space denoted by a search tree. The root node is ‘A’ and the goal node is ‘F’.

The depth-first traversal searches the tree in this order as depicted by the blue arrows.

A->B->E->F

In this particular scenario, it can be clearly seen that DFS find the goal node in less number of steps when compared to BFS (There can be situations where this would not hold as well).

Let’s now take a look at the advantages and disadvantages of the DFS search strategy.

 

 

 

Section C

 Question No.  1           Marks - 10

What is Artificial intelligence?          

 Options          

  1. Putting your intelligence into Computer
  2. Programming with your own intelligence
  3. Making a Machine intelligent
  4. Playing a Game

 

Question No.  2           Marks - 10

Which is the best way to go for Game playing problem?      

 Options          

  1. Linear approach
  2. Heuristic approach
  3. Optimal approach
  4. Random approach

 

 Question No.  3           Marks - 10

Which is not the commonly used programming language for AI?    

 Options          

  1. PROLOG
  2. Java
  3. LISP
  4. Perl

 

 Question No.  4           Marks - 10

Which search method takes less memory?     

 Options          

  1. Depth-First Search
  2. Breadth-First search
  3. Both (a) and (b)
  4. Linear Search.

 

 Question No.  5           Marks - 10

A heuristic is a way of trying 

 Options          

  1. To discover something or an idea embedded in a program
  2. To search and measure how far a node in a search tree seems to be from a goal
  3. To compare two nodes in a search tree to see if one is better than the other
  4. Only (a), (b) and (c).

 

Question No.  6           Marks - 10

What is state space?   

 Options          

  1. The whole problem
  2. Your Definition to a problem
  3. Problem you design
  4. Representing your problem with variable and parameter

 

 Question No.  7           Marks - 10

A production rule consists of 

 Options          

  1. A set of Rule
  2. A sequence of steps
  3. Arbitrary representation to problem
  4. Both (a) and (b)

 

 Question No.  8           Marks - 10

Who is called the father of AI?         

 Options          

  1. James C Gosling
  2. Dennis Ritchie
  3. Alan Turing
  4. Isaac Newton

 

 Question No.  9           Marks - 10

In AI ………………………. is a combination of data structures and interpretive procedures.      

 Options          

  1. Knowledge
  2. Meta-knowledge
  3. Artificial Knowledge
  4. Performance

 

 Question No.  10         Marks - 10

The …………………… approach uses the knowledge of mathematics and engineering.   

 Options          

  1. rationalist
  2. Top-down
  3. bottom-up
  4. push-pop approach

 

 Question No.  11         Marks - 10

We also use knowledge about what we know, called ……………………..

 Options          

  1. Meta-Knowledge
  2. Performance Knowledge
  3. Standard knowledge
  4. Specific knowledge

 

 Question No.  12         Marks - 10

The Artificial Intelligence is concerned with designing intelligent computer systems that exhibit intelligent characteristics expressed by …………………..        

 Options          

  1. Functional behaviour
  2. Human behaviour
  3. Human brain
  4. Statistical analysis

 

 Question No.  13         Marks - 10

State whether the following true or false. i) AI is used in diverse fields like space exploration, robotics. ii) AI is used for military purpose    

 Options          

  1. i-True, ii-False
  2. i-True, ii-True
  3. i-False, ii-False
  4. i-False, ii-True

 

 Question No.  14         Marks - 10

The goals of AI systems can be described in terms of cognitive tasks like   

 Options          

  1. Recognizing objects
  2. Answering questions
  3. Manipulating robotic devices
  4. All of the above

 

Question No.  15         Marks - 10

………………….. is computerized advice-giver, that is capable of reasoning but which is usually confined to a rather narrow field of knowledge.     

 Options          

  1. Expert system
  2. Knowledge system
  3. Common system
  4. Communication system

 

 Question No.  16         Marks - 10

One method of programming a computer to exhibit human intelligence is called modeling on____________.      

 Options          

  1. simulation
  2. cognition
  3. more intelligence.
  4. programming

 

Question No.  17         Marks - 10

Shaping teaching techniques to fit the learning patterns of individual students in the goal of _______________. 

 Options          

  1. Automatic programming.
  2. Decision support.
  3. Intelligent computer assisted instruction.
  4. Expert systems.

 

 Question No.  18         Marks - 10

What was originally called the imitation game by its creators?         

 Options          

  1. The Turing test.
  2. LISP
  3. Cyber entices.
  4. The logic theorist.

 

Question No.  19         Marks - 10

The term artificial intelligence was coined by _____________.       

 Options          

  1. John MC Carthy.
  2. Marvin minsky.
  3. Natheniel Rochester.
  4. Claude Shannon.

 

 Question No.  20         Marks - 10

Human brain has capacity to ____________.

 Options          

  1. programe
  2. learn
  3. Learn and program.
  4. read

 

 Question No.  21         Marks - 10

An expert system differs from a database program if only an expert system__________.  

 Options          

  1. contains declarative knowledge.
  2. contains procedural knowledge.
  3. features the ret rival of sorted information.
  4. excepts users to draw their own conclusions.

 

Question No.  22         Marks - 10

Which of the following component is an expert system?      

 Options          

  1. Inference engine.
  2. Knowledge base.
  3. User interface.
  4. GUI

 

 Question No.  23         Marks - 10

In rule based system procedural domain knowledge is the form of_____________.           

 Options          

  1. production rules.
  2. rule interpreters.
  3. control rules.
  4. meta rules.

 

 Question No.  24         Marks - 10

A________________is an integral part of developing an expert system.     

 Options          

  1. cognitive scientist.
  2. domain expert.
  3. knowledge engineer.
  4. software engineer.

 

 Question No.  25         Marks - 10

A process that is repeated, evaluated, and refined is called___________.   

 Options    

  1. descriptive
  2. diagnostic
  3. interpretive
  4. interative

 

  Question No.  26         Marks - 10

A problem is first connected to the proposed solution during the_____________ stage.    

 Options          

  1. identification
  2. conceptualization
  3. formalization
  4. implementation

 

Question No.  27         Marks - 10

Which of the following is the heuristic evaluation function?

 Options          

  1. hill climbing search.
  2. breadth first
  3. depth first
  4. shortest path

 

 Question No.  28         Marks - 10

Which is one of the brute force search technique.     

 Options          

  1. depth first search
  2. heuristic search
  3. best first search
  4. beam search

 

 Question No.  29         Marks - 10

The speed of present day computers is expressed in the terms of ___________.     

 Options          

  1. million Instructions per hour.
  2. million Instructions per second.
  3. million Instructions per day.
  4. million Instructions per minute.

 

Question No.  30         Marks - 10

The most common methods of problem representation in AI are_____________.   

 Options          

  1. state space representation and problem.
  2. state space representation and representation.
  3. state space representation problem representation.
  4. state space representation and problem representation.

 

Question No.  31         Marks - 10

Basically the knowledge of an expert occupies three distinctive layers____________.       

 Options          

  1. factual knowledge, heuristic knowledge .
  2. factual knowledge, meta knowledge.
  3. factual knowledge, heuristic knowledge, meta knowledge.
  4. heuristic knowledge, meta knowledge.

 

 Question No.  32         Marks - 10

LISP MACHINES also known as____________.    

 Options          

  1. AI workstations.
  2. super minicomputers.
  3. time sharing terminals.
  4. client machines.

 

 Question No.  33         Marks - 10

Which of the following is an advantage of using an expert system development tool?       

 Options          

  1. Knowledge Engineering.
  2. Imposed Structure.
  3. Rapid Prototyping.
  4. protocol

 

 Question No.  34         Marks - 10

Which search technique imposes a fixed depth limit on a depth-first search.           

 Options          

  1. Depth first search
  2. Depth limited search.
  3. Breadth first search.
  4. Breadth limited search

 

 Question No.  35         Marks - 10

The search which proceeds immediately to the deepest level of the search tree is _____________.           

 Options          

  1. uniformed Search.
  2. . breadth first search.
  3. . depth first search.
  4. uniform cost.

 

Question No.  36         Marks - 10

The simple strategy in which the root node is expanded first then all the successors and so on .this is called _____.           

 Options          

  1. informed search.
  2. breadth first search.
  3. uniform cost search.
  4. linear search.

 

 Question No.  37         Marks - 10

The problem which aims to find the shortest tour in which each city must be visited exactly once is ________.   

 Options          

  1. robot navigation.
  2. traveling salesperson problem.
  3. nternet searching.
  4. search tree

  

Question No.  38         Marks - 10

A problem which is intended to illustrate or exercise various problems solving methods which give a concise exact description?    

 Options          

  1. Real-world problem.
  2. Toy problem
  3. 8-puzzle problem.
  4. 8-queen problem.

 

 Question No.  39         Marks - 10

Which will provide patterns for arguments structures that always yielded correct conclusion when given premises is correct?          

 Options          

  1. logic
  2. syllogisms
  3. philosophy
  4. Rational agent

 

 Question No.  40         Marks - 10

The last component of the learning agent is __________.    

 Options          

  1. agent generator
  2. program generator.
  3. problem generator.
  4. output generator

  

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