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Artificial Intelligence 05102 BE Information Technology SAMPLE Question For Chapter 3 Chapter 4 Chapter 5 Chapter 6 Artificial Intelligence Chapter 3 1. (a) List the type of knowledge include in expertise. (8 marks) (b) List the eight activities of human expert. (8 marks) 2. (a) What is the role of knowledge engineer. (6 marks) (b) Briefly explain about Human elements in Expert system. (10 marks) 3. (a) List the ten generic categories of ES. (10 marks) (b)Define the Es development environment and contrast with consultation environment. (6 marks) 4. List the type of Expert system. Define them. (16 marks) chapter (4) 5. (a) Describe the process of protocol analysis. (10 marks) (b) List several sources of knowledge. (6 marks) 6. What is the RGA and describe the process of RGA‟s work. (16 marks) 7. Describe the process of automated rule induction and write down benefits of it. (16 marks) 8. (a) Give four reason why knowledge acquisition is difficult. (10 marks) (b) What are the major desired skills of a knowledge engineer. (6 marks) chapter5 9. Define semantic network and give two advantages and two limitations. (16 marks) 10. (a) compare and contract the production rule and frame. (10 marks) (b) List three types of facets of a frame and explain their meaning. (6 marks) 11. (a)Describe advantages and disadvantages of rule representation. (10 marks) (b) What is a slot in a frame. (6 marks) 12. Briefly explain about script. Develop a script about restaurant. (16 marks) 13. (a)Define a list and give an example. (10 marks) (b)List the major knowledge representation method. (6 marks) 14. Prepare a set of frames of an organization given the following information. (16 marks) Company : 1050 employees,$130 million annual sales, Jan fisher is the president Department : accounting, finance, marketing, production, personnel Production department : five lines of production Product : computers Annual budget : $50,000+$12,000 × number of computers produced Materials : $6,000 per unit produced Working days : 250 per year Number of supervisors : one for each twelve employees Range of number of employees : 400-500 per shift (two shifts per Day) overtimes or part time on a third shift is possible chapter (6) 15. (a) Define deductive reasoning and contrast it with inductive reasoning. (10 marks) (b) What is meant that a rule „fires‟. (6 marks) 16. (a)Define model-based reasoning. (8 marks) (b)Define case-based reasoning. (8 marks) 17. (a)List the name of the purpose of explanation capability. (10 marks) (b)Define static explanation. (6 marks) 18. (a)Briefly explain about inference tree. (10 marks) (b)Define meta rule. (6 marks) 19. You are given a set of rules for this question as follow. (16 marks) Goal : whether or not to invert in IBM stock. R1 : If a person has $10,000 and she has a college degree, THEN she should invest in securities R2 : If a person‟s annual income is at least $40,000 and she has a college degree, THEN she should invest in growth stocks. R3 : If a person is younger than thirty and if she is investing in securities, THEN she should invest in growth stocks R4 : If a person is younger than thirty, THEN she has a college degree R5 : If a person wants to invest in growth stock, THEN the stock should be IBM Run a backward chaining with the facts : The investor has $ 10,000 and She is twenty – five year old 20. You are given a set of rules for this question as follows. (16marks) R1 : If inflation is low THEN interest rates are low ELSE interest rates are high. R2 : If interest rates are high THEN housing prices are high R3 : If housing prices are high THEN do not buy a house ELSE buy it Run a forward chaining with the fact : Low inflation : rate as given Artificial Intelligence Sample Question Solution Chapter3 1. (a) List the type of knowledge included in expertise . (8 marks) Expertise Expertise is the extensive, task-specific knowledge acquire from Training, reading and experience. Expertise includes: -Facts about the problem area - Theories about the problem area -Hard and fast rules and procedures regarding the general problem area. -Rules of what to do in a given problem situation. -Global strategies for solving these types of problems. - Meta knowledge (knowledge about knowledge) (b) List the eight activities of human expert. (8 marks) Human ex parties includes a constellation of behavior that Involves the following activities: Involves the following activities: - Recognizing and formulating the problem - Solving the problem quickly and properly - Explaining the solution - Learning from experience - Restricting knowledge - Breaking rules - Determining relevance - Degrading gracefully 2. (a) what is the role of knowledge engineer. (6marks) The knowledge engineer helps the experts structure the problem Area by interpreting human answer to question drawing analogies, Posing counterexamples and bringing conceptual difficulties. He or She is the system builder. (b)Briefly explain about Human elements in Expert system. (10 marks) The Expert The expert, commonly referred to as the domain expert and Methods along with the ability to apply these talents give advice and Solve problems. The experts knows which facts are important and understands the meaning of the relation among facts. The knowledge Engineer The knowledge engineer helps the experts structure the problem area by interpreting and integrating human answers to questions, drawing analogies, posing counterexamples and bringing to light conceptual difficulties. He or she is the system builder. The User Most computer-based systems have evolved in a single-user mode. In construct, the ES has several possible types of users: -A no expert client seeking direct advice. In such a case the ES acts As a consultant or advisor. -A student who wants to learn. In such a case the ES acts as an instructor. An ES builder who instructor. An ES builder who wants to improve or increase the knowledge base . In such a case the ES acts as a partner. -An expert .In such a case the ES acts as a colleague. Other Participants Several other participants may be involved in ES. A system builder may assist in integrating the expert system with other computerized systems. A tool builder may provide generic or build specific tools. A vendors may provide tools and advice and support staff may provide clerical and technical help. 3. (a) List the ten generic categories of: ES (10 marks) Generic categories of Expert system Category Problem Addressed Interpretation Inferring situation descriptions from observations Prediction Inferring likely consequences of given situations Diagnosis Inferring system malfunctions form observations Design Configuring object under constraints Planning Developing plan to achieve goal(s) Monitoring Comparing observations to plans, flagging exceptions Debugging Prescribing remedies for malfunctions Repair Executing a plan to administer a prescribed ream Instruction Diagnosing, debugging and correcting student performance Control Interpreting, predicting, repairing and monitoring system behaviors. 3. (b) Define the ES development environment and construct with Consultation environment. (6 marks) Expert systems are composed of two major parts : the Development environment and consolation environment. The development environment is used by the ES builder to build the components to introduce knowledge into the knowledge base .The consultation environment is used by a non expert to obtain expect knowledge and advice. 4. List the type of expect system .Define them. Types of Expert Systems. (16 marks) Expert Systems Versus Knowledge-based Systems ES is one whose behavior is so sophisticated that would call a person who performed in a similar manner an expert. In the commercial world, systems are emerging that perform effectively and efficiently tasks for execution do not need an expert. Such small systems are referred to as knowledge-based systems. Rule-based Expert System Many commercial ES are rule based, because the technology of rule-based systems is relatively well developed. In such systems the knowledge is represented as a series of production rules. Frame-based Systems In these systems, the knowledge is represented as frames, a representation of the object-oriented programming approach. Hybrid Systems These systems include several knowledge representation approaches, at minimum frames and rules but usually much more. Model-based Systems Model-based systems are structured around a model that simulates the structure and function of the system under study. The model is used to compute values which are compared to observed ones. Systems Classified By Their Nature The system leads the user to a structured selection from among a reasonable number of possible outcomes or actions. Ready-made Systems Ready-made systems are similar to application packages like an accounting general ledger or project management in operations management. Ready made systems enjoy the economy of mass production and therefore are considerably less expensive than customized systems. Real-Time Expert Systems Real-time systems are systems in which there is a strict time limit on the system‟s response time, which must be fast enough for user control the process being computerized. The system always produces a response by the time it is needed. Chapter (4) 5. (a) Describe the process of protocol analysis Process tracking refers to a set of techniques the attempt to track the reasoning process of an expert. Tracking methods can be Informal or formal. The most common formal method is protocol analysis. Protocol analysis particularly a set of techniques known as verbal protocol analysis, is a common method by which the knowledge engineer acquires detailed knowledge from the expert. A protocol is a rcord or documentation of the expert‟s step-by-step information processing and decision making behavior. In this method, which is similar to interview but more formal and systematic, the expert is asked to perform real task and to verbalize his or her thought process .The expert is asked by the knowledge engineer to “think aloud” while performing the task or solving the problem under observation .Usually, a recording is made as the expert thinks aloud, it describes every aspect of the information processing and decision making behavior. This recording then becomes a record, or protocol, of the expert‟s ongoing behavior. Later, the recording is transmitted for further analysis and coded by the knowledge engineer. 5. (b)List several sources of knowledge. A representative list of sources includes books, films, computer databases, pictures, maps, flow diagrams, stories, songs, or observed behavior .These sources can be divided into two types: documented and undocumented. The latter resides in people minds . Knowledge can be identified and collected by using any the human senses. 6. What is the RGA and describe the process of RGA‟s work. RGA Experts may also be confused between facts and facts that actually influence decision making. To overcome these one other limitations of knowledge acquisition by gaining insight in the expert‟s metal model of the problem domain, a number dictation technique shave been developed. These techniques decision from psychology. Since they are fairly structured, when applies AI technologies, these methods are usually aided by a computer. The primary method is repertory grid analysis (RGA). The Process of RGA‟s work. The RGA works according to several processes. First, the expert identifies the important objects in the domain of expert. Second, the expert identifies the important attributes that are considered in making decisions in the domain. Third, for each attribute the expert is asked to establish a bipolar scale with distinguishable characteristics and their appositives. Fourth, the interviewer picks any three of the objects an asks .What attributes and traits distinguish any two of these objects from the third? Attributes Traits Opposite Availability Widely available Not available Ease of programming High Low Training time Low High Orientation Symbolic Numeric Attribute Orientation Ease of Training Availability Programming Time Trait Symbolic(3) High (3) High(3) High (3) Opposite Numeric (1) Low (2) Low (1) Low (1) Lisp 3 3 1 1 PROLOG 3 2 2 1 C 2 3 2 2 COBOL 1 2 1 3 Example of grid. 7. Describe the process of automated rule induction and write down benefits of it. Automated rule induction Induction means a process of reasoning from the specific to the general .In ES terminology it refers to the process in which rules are Generated by a computer program from example cases. A rule induction system is given example of a problem where the outcome is known. After it has been given several examples, the rule induction system can create rules that fit the example cases. The rules can be used to asses other case where the outcome is not known. The heart of a rule induction system is an algorithm, which is used to induce the rules from the examples. Advantages of Rule Induction As the domain gets bigger and more complex, experts become usable to explain how they operate. They can however still supply the knowledge engineer with suitable examples of problems and solutions. Using rule induction allows ES to be user in more complicated and more commercially rewording fields. Another advantage is that the builder does not have to be a knowledge engineer. He or she can be the expert or a system analyst. This not only saves and money but it also solves the difficulties of dealing with the knowledge engineer who is an outside unfamiliar with the business. Machine induction also offers the possibility of deducing new knowledge. A big advantage of the rule induction is that it enhance the thinking process of the expert. 8. (a) Given fours reasons why knowledge is difficult. Expressing the knowledge to solve a problem a human expert Executes a two step process. First, the expert inputs information about the external world into the brain. The expert uses an inductive, deduction or other problem solving approaches on the information. It may be very difficult for the expert to express his or her experiences are made up of sensations, thoughts, sense memories and feelings. Transfer to a Machine-Knowledge is transferred to a machine where it must be organized in a particular manner. The machine requires the knowledge to be expressed explicitly at a lower, more detailed level than humans use. Human knowledge exists is compiled format. Number of participants .In a regular transfer of knowledge there two participants. In AI there could be as many as four participants the expert, the knowledge engineer, the system design and the user. These participants have different backgrounds, use different terminology and process different skills and knowledge structuring the knowledge. In AI it is necessary to elicit not only the knowledge but also its structure. We have to repress the knowledge in a structured way. 8. (b) What are the major desired skills of a knowledge engineer. Computer skills Tolerance and ambivalence Broad education Effective communication abilities Advanced, socially sophisticated verbal skills Fast learning capabilities Understanding of organizations and individuals Wide experience in knowledge engineering Intelligence Empathy and patience Persistence Logical thinking Versatility and inventiveness Self-confidence Chapter (5) 9. Define semantic network and give two advantages and two limitations (16.marks ) Semantic network is composed of nodes and links. Semantic networks are basically graphic depictions of knowledge that show hierarchical relationships between objects. It is made up of a number of circles or nodes represent object. Nodes can also be concepts, events or actions and attributes of an object. The might represent size, color, age, origin or other characteristics. The nodes in a semantic network are also interconnected by link arcs, show the relationships between the various objects and descriptive actors. Most common arcs are of the is –a or has-a type .Is-a show class relation that an object belongs to a larger class. Has –a links to identify characteristics or attributes of the object nodes. Two advantages are- The semantic net offers flexibility in adding new nodes and links to a definition as needed. The visual representation is easy to understand. The semantic net function in a manner similar to that of human information storage. Two limitations are- Two standards exist for the definition of nodes or relationships between and among nodes. Procedural knowledge is difficult to represent in a semantic net, since sequence and time are not explicitly represented. 10. (a) Compare and contrast the production rule and frame (10 marks) Production systems are developed by Newell and Simon for their model of human cognition. The production systems are modular knowledge representation schemes in these systems knowledge is presented as in the form of condition-action pairs: “If this condition occurs, THEN some action will occur. If it is hot, THEN. -If the stop light is red AND you have stopped, THEN aright turn is okay. A frame is a data structure that includes all the knowledge about a particular object. In a frame, knowledge is organized in a special hierarchical structure that permits a diagnosis of knowledge independence. Frames are basically an application of object-oriented programming for AI and ES. 10. (b) Compare and contrast the production rule and frame. Values : These describe the attributes such as blue, red and yellow for a co slot. Default : This facet is used if the slot is empty, that is, without any description Range : Range indicates and disadvantages of information can appear in a slot . 11. (a)Describe advantages and disadvantages of rule representation. Rules are easy to understand .They are communicable because they are communicable because they are a natural form of knowledge. Inference and explanations are easily derived. Modifications and maintenance are relatively easy: Complex knowledge requires many, many rules. This may create problems in both using the system and maintaining it. Builders like rules; therefore they try to enforce all knowledge all knowledge into rules rather than looking for more appropriate representations. 11. (b) What is a slot in a frame . (6 marks) A frame includes two basic elements: slots and facets. A slot is a set of attributes that describe the object represented the frame. Each slot contains one or more facets. The facets describe the knowledge or procedures about the attribute in the slot. 12. Briefly explain about script. Develop a script about restaurant. (16 marks) A script is a knowledge representation scheme similar a frame, but instead of describing an object, the script describes a sequence of events. Like the frame, the script portrays a stereotype Situation: Unlike the frame, it is usually presented in a piratical or context. To describe a sequence of events, the script uses a series of slots containing information about the people, objects and actions they are involved in the events. Track: Fast-food restaurant Roles: Customer (c) Server(s) Props: Counter Tray Food Money Napkins Salt/ Pepper/ Catsup/ Straws Entry conditions: Customer in hungry Customer has money Scene 1: Entry Customer parks car. o Customer enters restaurant. o Customer waits in line at the counter. Customer reads the menu on the wall and makes a decision about what to order. Scene 2: Order Customer gives order to server. Server fills order by putting food on tray. Customer pays server. Scene 3: Eating Customer gets napkins, straws, salt, etc. Customer takes tray to an unoccupied table. Customer eats food quickly. Scene 3A (option): Take-out Customer takes food and exists. Scene 4: Exit Customer cleans up table. Customer discards trash. Customer leaves restaurant. Customer drives away. Results: Customer is no longer hungry. Customer has less money. Customer is happy. Customer is unhappy. Customer is too full. Customer has upset stomach. Options 13. (a ) Define a list and give an example. (10 marks) Lists A lists is a series of related items. It can be a list of names of things, products. List are namely used to represent knowledge in while objects are grouped, categorized or graded according to rank or relationship objects are first divided into groups or classes of similar items. Their relationships are shown by linking them together. The simplest form is one list a hierarchy is created when two or more related Lists are combined Figure 5.9 shows a generalized format for a list. The List has name to identify it and two or more elements. You can see an element in one list can be the name of another list containing sub elements. List A Element 1 2 Element 2 3 Subelement a 4 b 5 c 6 d Element 4 Subelement a Subelement b Subelement c Subelement c Sub –subele (1) (2) Figure 5.9 List Representing Hierorchical Knowledge 13. (b) List the major knowledge representation method. (6 marks) Major knowledge – 1. Representation in logic 2. Analysis representation Semantic Network Scripts Lists Decision tables Decision trees 3 Coding Representation Production rules Frames 14. Prepare a set of frames of an organization given the following information. (16 marks) Company : 1050 employees, $130 million annual sales, Jan fisher is the president Departments : accounting, finance, marketing, production, personnel Production department : Five lines of production Product : Computer Annual budget : $ 50,000+$ 12,000* number of computer production Materials : $ 6,000 per unit produced. Working days : 250per year Number of supervisors : one for each twelve employees Range of number of employees: 400-500 per shift ( two shifts per overtime or part time on a third shift is possible. Name Company Name Production Employees 1050 Lines of production 5 Annual sales $ 130 Product Computer President John fisher Has a Name Finance Hierorchy Department Annual Name Department budge IS-a Material $600 per unit IS-a IS-a IS-a IS-a Name Annual budget Equation Name Personal department From $50000 Working –days 250 per year +$120000 No: of supervisors 88 *no:of computer No: of shift 3 produce Third shift Overtime Range of employee 400-500 per shift Chapter (6) 15. (a) Define deductive reasoning and contrast it with reasoning. Deductive reasoning Deductive reasoning is a process in which general premises are used to obtain a specific inference. Reasoning moves form a general principle to a specific conclusion. The deductive process generally begins with a statement of the premises and conclusions. It consists of three parts: a major premise, a minor premise, and a conclusion. Major premise : I do not job when the temperature exceeds 90 degrees. Minor premise : Today the temperature is 93 degrees. Conclusion : Therefore, I will not jog today. The whole idea is develop new knowledge from previously given knowledge. Inductive Reasoning Inductive reasoning uses a number of established facts or premises to draw some general conclusion. Premise : Faulty diodes cause electronic equipment failure. Premise : Defective transistors cause electronic equipment failure. Premise : Defective integrated circuits cause electronic equipment malfunction. Conclusion may be difficult to arrive at, or it may never be final on absolute. Conclusions can charge if new facts are discovered. The more knowledge you have, the more conclusive your inference can be. 15. (b) What is meant that a rule „fires‟. We say that Rule “fires”. Firing a rule occurs only when all of the rule‟s parks are satisfied. Then, the conclusion draw is stored in the assertion base. 16. (a) Define model-based reasoning. Model-based reasoning Model-based reasoning is based on knowledge of the structure and behavior of the devices the system is designed to understand. Model based systems are especially useful in diagnosed equipment problems. The systems include a model of the device to be diagnosed that is then used to identify the cause(s) of the equipment failure. Because they draw conclusions directly from knowledge of a device‟s structure and behavior, model-based expert systems are said to reason from “first principles”. 16. (b) Define case-based reasoning Case – based reasoning The basic idea of case-based reasoning is to adapt solutions that were used to solve old problems and use them for solving new problems. One variation of this approach is the rule-induction method. In rule induction the computer examines historical cases and generates rules, which are chained to solve problems. Case-based reasoning follows a different process Finds those cases in memory that solved problems similar to the current problem, and-adapts the previous solution or solution to fit the current problem, taking into account any difference between the current and previous situations. 17. (a) List name of the purposes of explanation capability. The explanation facility has several specific purposes: - Make the system more into huggable to the user. - Uncover the shortcomings of the rules and knowledge base. - Explain situations that were unanticipated by the user. - Satisfy psychological and / or social needs by helping a user feel more assured about the actions of the ES. - Clarify the assumptions underlying the system‟s operations, both to the user and the builder. - Conduct sensitivity analyses. 17. (b) Define static explanation. There are different methods for generating explanations. An easy way to do them is to pre-insert pieces of English text in the system. For example, each question that could be asked by the user may have an answer test associated with it. This is called static explanation. 18.(a) Briefly explain about inference tree. The inference tree (also goal tree, or logical tree) provides a schematic view of the inference process. It is similar to a decision tree. Note that each rule is composed of a premise and a conclusion. In building the inference free the premise and conclusion. In building the inference free the premises and conclusions are shown as nodes. The branches connect the premises and conclusions. The operators AND and OR are used to reflect the structures of the rules. By using the tree, we can visualize the process of inference and movement along the branches of the tree. This is called tree traversal. The inference tree is constructed upside down. The root is at the top and the branches point downward. The tree ends with “leaves” at the buttom. Single inference trees are always a mixture of AND nodes and OR nodes; they are often called AND /OR trees. (b) Define metarule. Metarule Conflict resolution is done in may cases by introducing inference rules, for example, deciding about which rules to use next. In such a case, we deal with metarules, or rules about rules. Inference engines that include metarules are more complex than those that do not. Futhermore, it is worth nothing that metarules also make the knowledge base harder to read and understand. 19. You are given a set of rules for this question as follows. (16 marks) Goals : wheather or not to invert in IBM stock. R1 : If a person has $10,000 and she has a college degree, THEN should invest in securities. R2 : If a person‟s annual income is at least $ 40,000 and she has a college degree, THEN she should invest in growth stocks. R3 : If a person is younger than thirty and she is investing in securities THEN she should invest in growth stocks. R4 : If a person is younger than thirty, THEN she has a college degree. R5 : If a person wants to invert in growth r tock, THEN the stock should be IBM. Run a black work chaining with the facts: invertors has $ 10,000 and she is twenty-five year old. Ans: A= Have $10,000 B= Younger than thirty C= Education at college level D= Annual income of at least $ 40,000 E= Invert in securities F= Invert in growth stocks G= Invert in IBM stock R1: IF A and C, THEN E R2: IF D and C, THEN F R3: IF B and E, THEN F R4: IF B THEN C R5: IF F, THEN G Fact: invertor has $ 10,000 She is twenty-five years old A is true. B is true. 19. Ans . Goal: whether or not to invert in IBM stock : G or not Staring point : We start by looking for a rule that include the goal CG .In its conclusion( THEN ) part. Step(1) : Try to access (or) reject (G). In a assertion base. A is true B is true G is not in assertion base but is in conclusion of rul Step(2) : R(5) says that if F is true, THEN G is true. F is not assertion base but F is in conclusion rule R2 and R3. Step(3) : We try R2 first. In R2, if both D and C are true, THEN F is true. D is not in conclusion of any rule nor or fact. So we return to another rule R3. Step(4) : R3 says that if both B and E are true, THEN F is true. Because it is given Fact, B is true. E is not in assertion base. E is a conclusion of R1. Step(5) : R1 says that if both A and C are true, THEN E is true. A is true because it is given fact. C is no in assertion base but is in conclusion of R1. Step(6) : R4 says that if B is true, THEN C is true. C is true because B is given fact. C become a fact and added to assertion base. Step(7) : E is true because A and C are true. R1 fired and which valid F. So R3 is fired. F is true, THEN G is true. ES will recommend to invert in IBM stock. 20. You are given a set of rules for this question. Goals: should we buy a house or not? R1: If inflation is low. THEN interest rates are low. Else interest rates are high. R2: If interest rates are high. THEN housing prices are high. R3: If housing prices are high. THEN do not buy a house. ELSE buy it. Run a forward chaining with a high inflation rate as given. Ans: A= Inflation is law B= Interest rates are low. C= Interest rates are high. D= Housing prices are high. E= Do not buy a house. F= Buy it. R1:IF A THEN B ELSE C R2:IF C THEN D step 2 R3:IF D THEN E ELSE F step 3 Forward chaining Fact: Law inflation rate (A is true) Goal: To buy a house or not (E or F) Starting point: start from fact A. We look for a rule that includes an A in the IF side of rule. This is R1. 20. Ans: Step 1 : R1 says that If A is true then B is true then B is true else c is true A is true in assertion base, So B is ture and C is Flase. R1 fires. C is a premise of R2. Step 2 : R2 that if c is true, then D is true. D is False because C is false. R2 fires. D is added to assertion base. D is a premise of R3. Step3 : R3 says that if D is true then E is true else F is true. D is false in assertion base. So, F is True. Es will recommend to buy a house.
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