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CUSTOMER_CODE SMUDE DIVISION_CODE SMUDE EVENT_CODE JAN2016 ASSESSMENT_CODE BT9402_JAN2016 QUESTION_TYPE DESCRIPTIVE_QUESTION QUESTION_ID 13592 QUESTION_TEXT Explain the different fundamental principles considered in artificial intelligence. SCHEME OF EVALUATION i.The first principle: Bounded rationality implies opportunities search: This is the fundamental result of Simon’s research on decision making in organizations, leading to the Nobel prize. ii.The second principle: A physical symbol system is necessary and sufficient for intelligent action: The second principle of AI is the physical system hypothesis, i.e. a physical symbol system is necessary and sufficient for intelligent action. iii.The third principle: the magic no 70,000+20,000: The third principle of AI is that an expert knows 70,000 counts of information give or take a binary order of magnitude. iv.Fourth principle: Search compensate for lack of knowledge: The fourth principle of AI is that search compensate for lack of knowledge. v.The fifth principle: knowledge compensate for lack of search: - This is an important insight which was clearly understood even as late as 1970 i.e. knowledge reduces uncertainty and helps us constrain the exponential growth leading to the solution of the many otherwise unsolved problems.(2 marks each) QUESTION_TYPE DESCRIPTIVE_QUESTION QUESTION_ID 13596 QUESTION_TEXT Explain various building blocks of expert systems. SCHEME OF EVALUATION i.Knowledge base: The knowledge base of expert systems contains both factual and heuristic knowledge. Factual knowledge is that knowledge of the task domain that is widely shared. Heuristic knowledge is the less rigorous, more experiential, more judgmental knowledge. ii.Reasoning or inference process/ engine: The problem solving model or paradigm organizes & controls the steps taken to solve the problem. iii.Explaining how or why: The explanation module provides the user with an explanation of the reasoning process when requested. This is done in response to a how query or a why query. iv.Building a knowledge base: The editor is used by developers to create new rules for addition to the knowledge base, to delete outmoded rules, or to modify existing rules in some way. v.The I/O interface: It permits the user to communicate with the system in a more natural way by permitting the use of simple selection menus or the use of restricted language which is close to a natural language. (2 marks each) QUESTION_TYPE DESCRIPTIVE_QUESTION QUESTION_ID 72846 QUESTION_TEXT Write any five Features of Expert System SCHEME OF EVALUATION (2*5=10 marks) Goal driven reasoning or backward chaining–an inference technique which uses if THEN rules to repetitively break a goal into smaller sub goals which are easier to prove. Coping with uncertainty: the ability of the system to reason with rules and data which are not precisely known. Data driven reasoning or forward chaining – an inference technique which uses IF THEN rules to deduce a problem solution from initial data. Data representation– the way in which the problem specific data in the system is stored and accessed User interface –that portion of the code which creates an easy to use system Explanations –the ability of the system to explain the reasoning process that it used to reach a recommendation. QUESTION_TYPE DESCRIPTIVE_QUESTION QUESTION_ID 72847 QUESTION_TEXT Write any five guidelines to determine whether a problem is suitable for an expert system solution. SCHEME OF EVALUATION (2*5=10 marks) Ans: The need for a solution must justify the costs involved in development. There must be a realistic assessment of the costs and benefits involved. Human expertise is not available in all situations where it is needed. If the expert knowledge is widely available it is unlikely that it will be worth developing an expert system The problem may be solved using symbolic reasoning techniqures. It should’nt require manual dexterity or physical skill. The problem is well structured and does not require common sense knowledge. Common sense knowledge is notoriously hard to capture and represent. The problem cannot be easily solved using more traditional computing methods. If there is a good algorithm solution to a problem, you don’t want to use an expert system Cooperative and articulate expert exist. For an expert system project to be successful it is essential that the experts are willing to help and don’t feel that their job is threatened. The problem is a proper size and scope. Typically you need problems that require highly specialized expertise, but would only take a human expert a short time solve. QUESTION_TYPE DESCRIPTIVE_QUESTION QUESTION_ID 72848 QUESTION_TEXT List the architectural principles for Expert system. SCHEME OF EVALUATION (1*10=10marks) Knowledge is power Knowledge is often inexact and incomplete Knowledge is often poorly specified Expert system need to b flexible Expert systems need to be transparent Separate inference engine and knowledge base level Use uniform fact representation Keep inference engine simple Explicit redundancy Amateurs experts slowly. QUESTION_TYPE DESCRIPTIVE_QUESTION QUESTION_ID 126151 QUESTION_TEXT What is hill climbing strategy? Discuss its drawbacks and solutions. 1.pick a random point in the search space 2.consider all the neighbors of the current state 3.choose the neighbor with the best quality and move to that state 4.repeat 2 through 4 until all the neighboring states are of lower quality 5.return the current state as the solution state Drawbacks: SCHEME OF EVALUATION local maxima: a peak that is lower than the highest peak in the state space. Once on a local maximum, hill climbing will halt, even though there is a better solution plateau: area of the state space where the evaluation function is nearly fla. Hill climbing will do a random walk in such an area ridges: a ridge can have steeply sloping sides, so that the search reaches the top with ease, but on the top may slope gently toward a peak. Unless there happens to be operators that move directly along the top of the ridge the search may oscillate from side to side making little or no progress. Solutions: backtracking making big jumps applying multiple rules before testing