Fuzzy Logic Knowledge Acquisition Final

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<ul><li><p>7/29/2019 Fuzzy Logic Knowledge Acquisition Final</p><p> 1/79</p><p>Topic: Fuzzy LogicKnowledge Acquisition andInterface Design</p><p>Group 3:Rahul Sharma</p><p>Sumar Loomba</p><p>Nisheeth Gupta</p><p>Pallavi SagneRitu Khushwaha</p><p>Prashanth R</p><p>Praveen Rathod</p><p>Rohan Dange</p><p>Rakesh Kumar 1</p></li><li><p>7/29/2019 Fuzzy Logic Knowledge Acquisition Final</p><p> 2/79</p><p>FUZZY LOGIC</p></li><li><p>7/29/2019 Fuzzy Logic Knowledge Acquisition Final</p><p> 3/79</p><p>WHAT IS FUZZY LOGIC?</p><p> Definition of fuzzy</p><p> Fuzzynot clear, distinct, or precise; blurred</p><p> Definition of fuzzy logic</p><p> A form of knowledge representation suitable for notions that</p><p>cannot be defined precisely, but which depend upon their</p><p>contexts.</p></li><li><p>7/29/2019 Fuzzy Logic Knowledge Acquisition Final</p><p> 4/79</p><p>TRADITIONAL REPRESENTATION OF</p><p>LOGIC</p><p>Slow Fast</p><p>Speed = 0 Speed = 1bool speed;</p><p>get the speed</p><p>if ( speed == 0) {</p><p>// speed is slow}</p><p>else {</p><p>// speed is fast</p><p>}</p></li><li><p>7/29/2019 Fuzzy Logic Knowledge Acquisition Final</p><p> 5/79</p><p>FUZZY LOGIC REPRESENTATION</p><p> For every problem</p><p>must represent in terms</p><p>of fuzzy sets.</p><p> What are fuzzy sets?</p><p>Slowest</p><p>Fastest</p><p>Slow</p><p>Fast</p><p>[ 0.00.25</p><p>]</p><p>[ 0.250.50 ]</p><p>[ 0.500.75 ]</p><p>[ 0.751.00 ]</p></li><li><p>7/29/2019 Fuzzy Logic Knowledge Acquisition Final</p><p> 6/79</p><p>FUZZY LOGIC REPRESENTATION CONT.</p><p>Slowest Fastestfloat speed;</p><p>get the speed</p><p>if ((speed &gt;= 0.0)&amp;&amp;(speed &lt; 0.25)) {</p><p>// speed is slowest</p><p>}</p><p>else if ((speed &gt;= 0.25)&amp;&amp;(speed &lt; 0.5))</p><p>{</p><p>// speed is slow</p><p>}else if ((speed &gt;= 0.5)&amp;&amp;(speed &lt; 0.75))</p><p>{</p><p>// speed is fast</p><p>}</p><p>else // speed &gt;= 0.75 &amp;&amp; speed &lt; 1.0</p><p>{</p><p>// speed is fastest</p><p>}</p><p>Slow Fast</p></li><li><p>7/29/2019 Fuzzy Logic Knowledge Acquisition Final</p><p> 7/79</p><p>ORIGINS OF FUZZY LOGIC</p><p> Traces back to Ancient Greece</p><p> Lotfi Asker Zadeh ( 1965 )</p><p> First to publish ideas of fuzzy logic.</p><p> Professor Toshire Terano ( 1972 )</p><p> Organized the world's first working group on fuzzy systems.</p><p>F.L. Smidth &amp; Co. ( 1980 ) First to market fuzzy expert systems.</p></li><li><p>7/29/2019 Fuzzy Logic Knowledge Acquisition Final</p><p> 8/79</p><p>FUZZY LOGIC IN CONTROL SYSTEMS</p><p> Fuzzy Logic provides a more efficient and resourceful</p><p>way to solve Control Systems.</p><p> Some Examples</p><p> Temperature Controller</p><p> AntiLock Break System ( ABS )</p></li><li><p>7/29/2019 Fuzzy Logic Knowledge Acquisition Final</p><p> 9/79</p><p>TEMPERATURE CONTROLLER</p><p> The problem</p><p> Change the speed of a heater fan, based off the room</p><p>temperature and humidity.</p><p> A temperature control system has four settings</p><p> Cold, Cool, Warm, and Hot</p><p> Humidity can be defined by:</p><p> Low, Medium, and High</p><p> Using this we can define</p><p>the fuzzy set.</p></li><li><p>7/29/2019 Fuzzy Logic Knowledge Acquisition Final</p><p> 10/79</p><p>BENEFITS OF USING FUZZY LOGIC</p></li><li><p>7/29/2019 Fuzzy Logic Knowledge Acquisition Final</p><p> 11/79</p><p>ANTI LOCK BREAK SYSTEM ( ABS )</p><p>Nonlinear and dynamic in nature</p><p> Inputs for Intel Fuzzy ABS are derived from</p><p> Brake</p><p> 4 WD</p><p> Feedback</p><p> Wheel speed</p><p> Ignition</p><p> Outputs</p><p> Pulsewidth</p><p> Error lamp</p></li><li><p>7/29/2019 Fuzzy Logic Knowledge Acquisition Final</p><p> 12/79</p><p>FUZZY LOGIC IN OTHER FIELDS</p><p> Business</p><p> Hybrid Modeling</p><p> Expert Systems</p></li><li><p>7/29/2019 Fuzzy Logic Knowledge Acquisition Final</p><p> 13/79</p><p>13</p><p>Fuzzy Logic and Knowledge</p><p>Based Systems (AI)</p><p>Knowledge Acquisition</p></li><li><p>7/29/2019 Fuzzy Logic Knowledge Acquisition Final</p><p> 14/79</p><p>What is Knowledge Acquisition?</p><p>Knowledge acquisition(KA) is the process of acquiringknowledge from a human expert for an expert system whichmust be carefully organized into if-then else rules or some otherform of knowledge representation. KA is the process of</p><p>absorbing and storing new information in memory, the successof which depends on how well the information can later beretrieved from memory. The process of storing and retrievinginformation depends heavily on the representation andorganization of the information.</p><p>27/02/2013 14</p></li><li><p>7/29/2019 Fuzzy Logic Knowledge Acquisition Final</p><p> 15/79</p><p>15</p><p>Knowledge Acquisition INTRODUCTION&amp; BACKGROUND</p><p> The important characteristics of knowledgeare that it isexperiential, descriptive, qualitative, largely undocumentedandconstantly changing.</p><p> There are certain domains where all these properties are found</p><p>and some where there are only a few.</p><p> The lack of documentation and the fact that experts carry a lot ofinformation in their heads, make it difficult to gain access to theirknowledge for developing information systems in general and</p><p>expert systems in particular.</p><p> Therefore, knowledge engineers have devised specialisedtechniques to extract and document this information in an efficient</p><p>and expedient manner: Knowledge Acquisition.</p></li><li><p>7/29/2019 Fuzzy Logic Knowledge Acquisition Final</p><p> 16/79</p><p>16</p><p>Knowledge AcquisitionINTRODUCTION&amp; BACKGROUND</p><p> Currently knowledge bases for knowledge based systems are crafted</p><p>by hand, this is a severe limitation on the rapid deployment of such</p><p>systems.</p><p> The automation of knowledge acquisition (from text) would greatly ease</p><p>this problem.</p><p> There is considerable interest in developing software tools which would</p><p>allow the automatic construction of knowledge bases from textualinformation.</p><p> This will provide the opportunity to rapidly build knowledge bases thus</p><p>increasing, for example, the rate at which knowledge based systems can</p><p>be developed and deployed</p><p> Knowledge acquisition can be regarded as a method by which aknowledge engineer obtains information from experts, text books,</p><p>and other authoritative sources for ultimate translation into a</p><p>machine language and knowledge base.</p><p> The person undertaking the knowledge acquisition must convert the</p><p>acquired knowledge into a form that a computer program can use.</p></li><li><p>7/29/2019 Fuzzy Logic Knowledge Acquisition Final</p><p> 17/79</p><p>17</p><p>Knowledge AcquisitionINTRODUCTION&amp; BACKGROUND</p><p>In the process of Knowledge Acquisition for an Expert System Project,</p><p>the knowledge engineer basically performs four major tasks in</p><p>sequence:</p><p>First, the engineer ensures that he or she understands the aims and objectives of</p><p>the proposed expert system to get a feeling for the potential scope of the</p><p>project.Second, he or she develops a working knowledge of the problem domain by</p><p>mastering it's terminology by looking up technical dictionaries and</p><p>terminology data bases. For this task the key sources of knowledge are</p><p>identified: textbooks, papers, technical reports, manuals, codes of practice,</p><p>and domain experts.</p><p>Third, the knowledge engineer interacts with experts via meetings or interviewsto acquire, verify and validate their knowledge.</p><p>Fourth, the knowledge engineer produces a "paper knowledge base"; a document</p><p>or group of documents which form an intermediate stage in the translation of</p><p>knowledge from source to computer program. This comprises the interview</p><p>transcripts, the analysis of the information they contain and a full descriptionof the major domain entities (e.g. tasks, rules and objects). </p></li><li><p>7/29/2019 Fuzzy Logic Knowledge Acquisition Final</p><p> 18/79</p><p>18</p><p>Knowledge AcquisitionINTRODUCTION&amp; BACKGROUND</p><p>Knowledge engineers interview experts in a specialist domain about how</p><p>he or she solves a given problem. Before interviewing the experts, the</p><p>knowledge engineers have to formulate their questions, and after the</p><p>interview the answers to the questions have to be analyzed.</p><p>The knowledge engineer has to familiarize himself or herself with the</p><p>terminology of the specialist domain; he or she has to consult technical</p><p>manuals, and in some cases learned papers, to see how the experts knowledge</p><p>is applied; the knowledge engineers sometimes consults textbooks or</p><p>encyclopedic texts for understanding the conceptual structure of the experts</p><p>domain.</p><p>In many different ways the knowledge engineer literally has to come to</p><p>terms with the language used by the expert and that used in the other</p><p>texts mentioned above. The knowledge engineer should become</p><p>conversant in the specialist language of his or her application domain.</p></li><li><p>7/29/2019 Fuzzy Logic Knowledge Acquisition Final</p><p> 19/79</p><p>19</p><p>Knowledge acquisition involveselicitation, analysis, modelling andvalidation of knowledge</p><p>1. Employing a technique to elicit data (usually verbal) from the expert.</p><p>2. Interpreting these verbal data (more or less skilfully) in order to inferwhat might be the expert's underlying knowledge and reasoning</p><p>process.</p><p>3. Using this interpretation to guide the construction of some model orlanguage that describes (more or less accurately) the expert'sknowledge and performance.</p><p>4. Interpretation of further data is guided in turn by this evolvingmodel.</p><p>5. The principle focus for the knowledge acquisition team should be inconstructing models, in domain definition, or problem identificationand problem analysis.</p></li><li><p>7/29/2019 Fuzzy Logic Knowledge Acquisition Final</p><p> 20/79</p><p>20</p><p>Roles for knowledge acquisition</p><p>Knowledge engineering and management: technological</p><p>innovation, ontology construction, document mark-up</p><p>AI systems development: generic methodologies (e.g., KADS: KADSstands for ``Knowledge Analysis and Documentation System''. Later on,other interpretations have been given to this acronym, such as``Knowledge Analysis and Design Support'. KADS is the name of a</p><p>structured methodology for the development of knowledge based systemsthat is now in practical use in many places in Europe and elsewhere.)</p><p>Organizational analysis:process approaches</p><p>Task analysis:job design</p><p>User analysis: generation of cognitive specifications for tasks, themitigation of human error in domains of risk or time pressure, theenhancement of proficiency through training and skill remediation</p><p>Requirements elicitation: systems or design analysis, conceptualdatabase design, software requirements definition</p></li><li><p>7/29/2019 Fuzzy Logic Knowledge Acquisition Final</p><p> 21/79</p><p>21</p><p>Preliminary Work I involves Reading,Observation, Discussion</p><p>Preliminary work is carried out by knowledge engineer(s)</p><p>Knowledge engineering is knowledge acquisition for expert systemdevelopment, and used to describe the reduction of a large body ofknowledge to a precise set of facts and rules</p><p> Knowledge engineer is a computer software programmer who gathersknowledge from experts and then translates the knowledge into the</p><p>knowledge base of a computerised expert system in a structured and logicalway, and eventually constructs computerised expert systems.</p><p> "Knowledge acquisition is a bottleneck in the construction of expertsystems.The knowledge engineer's job is to act as a go-between to help anexpert build a system. Since the knowledge engineer has far less knowledge</p><p>of the domain than the expert, however, communication problems impedethe process of transferring expertise into a program. The vocabularyinitially used by the expert to talk about the domain with a novice is ofteninadequate for problem-solving; thus the knowledge engineer and expertmust work together to extend and refine it. One of the most difficult aspectsof the knowledge engineer's task is helping the expert to structure the</p><p>domain knowledge, to identify and formalize the domain concepts."(Ref:</p></li><li><p>7/29/2019 Fuzzy Logic Knowledge Acquisition Final</p><p> 22/79</p><p>22</p><p>Preliminary Work - II</p><p>When acquiring knowledge about a domain it is absolutely crucial that the</p><p>knowledge engineer can converse with the expert using the expertterminology.</p><p>The knowledge engineer has to have a good grasp of the domain to be ableto ask intelligent questions to extract important and relevant knowledgefrom the experts who have vast amounts of knowledge a lot of which istacit knowledge.</p><p>The knowledge engineer must therefore do some preliminary workincluding research on the domain in question before the first interview withthe expert takes place.</p><p>Some requirements for KA Techniques</p><p> Take experts off the job for short time periods</p><p> Allow non-experts to understand the knowledge Focus on the essential knowledge</p><p> Try to capture tacit knowledge</p><p> Allow knowledge to be collated from different experts</p><p> Allow knowledge to be validated and maintained</p></li><li><p>7/29/2019 Fuzzy Logic Knowledge Acquisition Final</p><p> 23/79</p><p>23</p><p>The Knowledge Handbook</p><p>One of the functions of the knowledge engineer during the knowledgeacquisition phase is to document the knowledge that has been acquired. One</p><p>idea suggested (Wolfgram et. al. 1987 and others) is that of building aknowledge handbook.</p><p>Wolfgram et. al. describe the contents of the knowledge handbook as follows:</p><p> The general problem description.</p><p> Who the users are and their expectations from the system.</p><p> A breakdown of the problems into sub-problems and sub-domains forfuture knowledge acquisition.</p><p> A detailed description of the domain or sub-domain to be used for the</p><p>prototype.</p><p> A bibliography of reference documents.</p><p> A list of vocabulary, concepts, terms, phrases and acronyms in the domain.</p><p> A list of experts for the prototype.</p><p> Some reasonable performance standards for the system, based on</p><p>consultation with the experts and users.</p><p> Descriptions of typical reasoning scenarios gained from the knowledgeacquisition.</p></li><li><p>7/29/2019 Fuzzy Logic Knowledge Acquisition Final</p><p> 24/79</p><p>24</p><p>Basic knowledge engineering forknowledge acquisition - I</p><p>Knowledge engineer act as a go-between the expert and knowledge base.This can be achieved by means of eliciting knowledge from the expert,encoding it for the knowledge base, and refining it in collaboration with theexpert in order to achieve acceptable performance. The process is basicallyas follows:</p><p>The knowledge engineer interviews the expert to elicit his or herknowledge;</p><p>The knowledge engineer encodes the elicited knowledge for theknowledge base;</p><p>The shell uses the knowledge base to make inferences about particularcases specified by clients;</p><p>The clients use the shell's inferences to obtain advice about particularcases</p></li><li><p>7/29/2019 Fuzzy Logic Knowledge Acquisition Final</p><p> 25/79</p><p>25</p><p>Basic knowledge engineering forknowledge acquisition - II</p><p>Basic knowledge engineering model with manual acquisition of knowledgefrom an expert (left-hand side of the figure). This is also followed byinteractive application of the knowledge with multiple clients through anexpert system shell (right-hand side of the figure).</p></li><li><p>7/29/2019 Fuzzy Logic Knowledge Acquisition Final</p><p> 26/79</p><p>26</p><p>Interactive Knowledge Engineering forInteractive Knowledge Acquisition -I</p><p>In an interactive knowledge engineering process for interactive knowledge</p><p>acquisition, knowledge engineers have responsibility for: Advising the experts on the process of interactive knowledge</p><p>elicitation;</p><p> Managing the interactive knowledge acquisition tools, setting them upappropriately;</p><p> Editing the uuencoded knowledge base in collaboration with theexperts;</p><p> Managing the knowledge encoding tools, setting them upappropriately;</p><p> Editing the encoded knowledge base in collaboration with the experts;</p><p> Validating the application of the knowledge base in collaboration with</p><p>the experts; Setting up the user interface in collaboration with the experts and</p><p>clients;</p><p> Training the clients in the effective use of the knowledge base incollaboration with the expert by developing operational and training</p><p>procedures. </p></li><li><p>7/29/2019 Fuzzy Logic Knowledge Acquisition Final</p><p> 27/79</p><p>27</p><p>Interactive Knowledge Engineering forInteractive knowledge acquisition -II</p></li><li><p>7/29/2019 Fuzzy Logic Knowledge Acquisition Final</p><p> 28/79</p><p>28</p><p>Interactive Knowledge Engineering forInteractive Knowledge Acquisition -IIIInteractive knowledge acquisition and encoding tools can greatly reduce the</p><p>need for...</p></li></ul>

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