|
Perceptual computing is an application of Zadeh's theory of computing with words on the field of assisting people to make subjective judgments. ==Perceptual computer== The ''perceptual computer'' – ''Per-C'' – an instantiation of perceptual computing – has the architecture that is depicted in Fig. 1 ()–(). It consists of three components: encoder, CWW engine and decoder. Perceptions – words – activate the Per-C and are the Per-C output (along with data); so, it is possible for a human to interact with the Per-C using just a vocabulary. A vocabulary is application (context) dependent, and must be large enough so that it lets the end-user interact with the Per-C in a user-friendly manner. The encoder transforms words into fuzzy sets (FSs) and leads to a ''codebook'' – words with their associated FS models. The outputs of the encoder activate a Computing With Words〔Lotfi Zadeh (), the father of fuzzy logic, coined the phrase computing with words, and stated: ''“CWW is a methodology in which the objects of computation are words and propositions drawn from a natural language. (is ) inspired by the remarkable human capability to perform a wide variety of physical and mental tasks without any measurements and any computations. CWW may have an important bearing on how humans … make perception-based rational decisions in an environment of imprecision, uncertainty and partial truth.”'' He did not mean that computers would actually compute using words—single words or phrases—rather than numbers. He meant that computers would be activated by words, which would be converted into a mathematical representation using fuzzy sets (FSs), and that these FSs would be mapped by a CWW engine into some other FS, after which the latter would be converted back into a word. Zadeh’s definition of CWW is very general and does not refer to a specific field in which CWW would be used. ''Perceptual computing'' focuses on CWW for making subjective judgments.〕 (CWW) engine, whose output is one or more other FSs, which are then mapped by the decoder into a recommendation (subjective judgment) with supporting data. The recommendation may be in the form of a word, group of similar words, rank or class. Although there are lots of details needed in order to implement the Per-C’s three components – encoder, decoder and CWW engine – and they are covered in (), it is when the Per-C is applied to specific applications, that the focus on the methodology becomes clear. Stepping back from those details, the ''methodology of perceptual computing'' is: # Focus on an application (''A''). # Establish a vocabulary (or vocabularies) for ''A''. # Collect interval end-point data from a group of subjects (representative of the subjects who will use the Per-C) for all of the words in the vocabulary. # Map the collected word data into word-FOUs by using the ''Interval Approach'' (), (Ch. 3 ). The result of doing this is the ''codebook'' (or codebooks) for ''A'', and completes the design of the encoder of the Per-C. # Choose an appropriate CWW engine for ''A''. It will map IT2 FSs into one or more IT2 FSs. Examples of CWW engines are: IF-THEN rules (Ch. 6 ) and Linguistic Weighted Averages (), (Ch. 5 ). # If an existing CWW engine is available for ''A'', then use its available mathematics to compute its output(s). Otherwise, develop such mathematics for the new kind of CWW engine. The new CWW engine should be constrained〔This (new) constraint is the major difference between perceptual computing and function approximation applications of FSs and systems.〕 so that its output(s) resemble the FOUs in the codebook(s) for ''A''. # Map the IT2 FS outputs from the CWW engine into a recommendation at the output of the decoder. If the recommendation is a word, rank or class, then use existing mathematics to accomplish this mapping (Ch. 4 ). Otherwise, develop such mathematics for the new kind of decoder. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Perceptual computing」の詳細全文を読む スポンサード リンク
|