{"id":227,"date":"2018-07-13T10:52:16","date_gmt":"2018-07-13T10:52:16","guid":{"rendered":"http:\/\/itp6.epgpbooks.inflibnet.ac.in\/?post_type=chapter&p=227"},"modified":"2019-05-16T05:32:45","modified_gmt":"2019-05-16T05:32:45","slug":"using-fuzzy-logic-frames-and-semantic-netfor-knowledge-representation","status":"publish","type":"chapter","link":"https:\/\/ebooks.inflibnet.ac.in\/itp6\/chapter\/using-fuzzy-logic-frames-and-semantic-netfor-knowledge-representation\/","title":{"rendered":"Using Fuzzy logic, Frames and Semantic Netfor knowledge representation"},"content":{"raw":"
\"epgp<\/a>\r\n<\/span><\/div>\r\n
\r\n\r\n \r\n\r\nIntroduction<\/strong>\r\n\r\n \r\n

In this module we will see three more methods for knowledge representation, fuzzy logic, frames and semantic net. All of them extends the basic method of knowledge representation by providing solution to one or the other problem. Fuzzy logic help solve the problem of imprecision in data while frames represent the object and class representation and relationship, especially the inheritance issue. Semantic net describers and reasons with relations between various objects. Frames are used extensively while deploying AI based solutions using C++ and Java. Real world problem solutions requires to extends the singleton fame like structure to a frame system which is a collection of multiple frames connected to each other by some relation. The semantic net represents how each entity is connected with another and how one can reason by traveling over that link.<\/p>\r\n \r\n\r\nThe need for Fuzzy logic<\/strong>\r\n\r\n \r\n

In 26th module we have seen the issues with representing knowledge in predicate logic. One of the issue is the idea of relative degree of membership. Young teacher and young politician are two phrases using same word \u201cyoung\u201d but very different range of age for being young. Whenever human expert provides way to solve problems, we get similar issues. For example if we ask a housewife the rules for instructing washing machine how to wash clothes we might receive following instructions.<\/p>\r\n \r\n\r\n\u201cIf cloths are dirty use more washing powder\u201d\r\n\r\n\u201cIf cloths are heavier, rinse more\u201d\r\n\r\n\u201cIf cloths are dirty, use hot water\u201d\r\n\r\n\u2026.\r\n\r\n \r\n

How can we model such statements in a computer program? Please understand the complexity. What do we mean by dirty? With 10 grams of dust, or 20 or what? Or heavy? 5 kilos? 7? 8? What? In fact these rules are quite general and should work for any values between any valid and acceptable numbers. For an example a 7 kg washing machine might accept any weight from 1 to 7. Dirtiness might be decided on a scale of 1 to 10. The washing powder might have four scales, half a spoon to two spoons and so on.<\/p>\r\n \r\n

A logical answer to this problem is do somehow relate the weight value to some scale representing heaviness, dirtiness to a scale of 0 to 10 (or something similar) and so on and find out how these things are related and decide the amount of washing powder to be used or use rinsing cycles or choose program or use the temperature of the water to be used for washing.<\/p>\r\n \r\n

We need to augment predicate logic or whatever method we are using for knowledge representation using fuzzy logic to enable this. Here is some information on fuzzy logic and fuzzy sets.<\/p>\r\n\r\n<\/div>\r\n \r\n\r\nFuzzy sets and fuzzy logic<\/strong>\r\n\r\n \r\n

The conventional sets are sometimes denoted as crisp sets as the boundary between a member and a non-member is well laid out. For example a class student or class teacher are crisp classes. If you are a member or you aren\u2019t. Unlike that, the set like young student or fast car or heavy cloths aren\u2019t that precise about membership. A student with age 10 is definitely young but what about 25? 35? In fact you can say that the association with class \u201cyoung\u201d is reducing as the student gets older. You do not have an age value where student suddenly ceased to be a member of class young. You can also see that while we are discussing politicians, the range changes, a politician with an age of 50 is considered \u201cyoung\u201d unlike a student.<\/p>\r\n \r\n

Take the example of a fast car. A car with the speed of 150 kmph is definitely fast but what about 80kmph? Again, like young, while we are discussing car racing championship, 150 might not belong to a set of a fast car.<\/p>\r\n \r\n

We routinely place objects into such classes where the meaning is well understood but the boundaries are not well defined. Rather the boundaries are fuzzy. Some objects are definitely members while some other objects are partial members. The membership criteria changes as per context given. There is a gradual change from a member to a non-member.<\/p>\r\n \r\n

Such sets are known as fuzzy sets. If you look closely at the discussion that we had about washing machines, the sets like \u201cdirty\u201d, \u201cheavy\u201d, \u201crinse\u201d are all fuzzy sets. Dealing with such sets requires different processing than conventional crisp sets. The fuzzy logic (or fuzzy reasoning) is a detailed mathematical model for representing fuzzy classes and related operations1<\/a>.<\/p>\r\n \r\n

The extent to which an object is a member of a class is denoted by a quantity called membership grades. A membership grade is a value between 0 and 1. An object with membership grade 1 is definitely a member of a class while an object with membership grade 0 is definitely not. Any other value indicates how far that object is a member of that class. For example membership grade for a student with age 10 is 1 while membership grade for a student with age 35 may be 0.4. This values, like certainty factors, is based on expert\u2019s intuition and judgement. There is a difference though between a CF value and a membership grade. When we encounter two statements as follows. First, the rule has some uncertainty, the data is certain (if the patient has malaria, he has, if he does not have, he does not have it), we are uncertain about the rule being true. In the second case, the data is uncertain, the rule is perfectly certain, we are uncertain about degree to which our data matches the rule.<\/p>\r\n \r\n\r\nIf the patient has fever, there is a suggestive evidence (0.7) that he has malaria If the cloths are dirty, rinse more.\r\n\r\nUsing multiple Fuzzy Sets to implement rule\r\n\r\n \r\n\r\nWhat if the washing machine is running a typical wash program and encounters following rule\r\n\r\n \r\n\r\nIf the cloths are dirtier than normal, and also heavier than normal, pick up more washing powder and choose a longer rinse cycle.<\/em>\r\n\r\n \r\n

While this rule was being deliberate upon, the value of dirtiness is determined as 7.6 (from a scale of 0 to 10) while value of heaviness is determined as 4.25 by washing machine sensors. We need to execute the rule. The rule looks like one written in predicate logic but with values that will\u00a0determine the amount of washing powder and longevity of the rinse cycle requires more processing before the rule is applied.<\/p>\r\n\r\n