An arithmetic approach for the computing with words paradigm

Delgado, Miguel; Duarte, Oscar; Requeña, Ignacio

doi:10.1002/int.20123

Cited by 17 publications

(6 citation statements)

References 9 publications

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“…Prior to the late 1990s, this problem had been recognized only by a few authors, among them Eshragh and Mamdani (1979) and Novak (1989), but no investigation of it had been undertaken. More recently, the problem has been addressed more substantially by Dvorak (1999), Yager (2004), Klir and Sentz (2006), Delgado et al (2006). The purpose of this paper is to discuss the main issues involved in this problem and to examine several possible ways of dealing with it.…”

Section: Computing With Wordsmentioning

confidence: 98%

On the problem of retranslation in computing with perceptions

Martin

Klir

2006

International Journal of General Systems

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This paper deals with one problem that needs to be addressed in the emerging field known under the name "computing with perceptions". It is the problem of describing, approximately, a given fuzzy set in natural language. This problem has lately been referred to as the problem of retranslation. Two approaches to dealing with the retranslation problem are discussed in the paper, one based on a pre-defined set of linguistic terms and the associated fuzzy sets, and one based on approximating the given fuzzy set by another fuzzy set that is easier to describe in natural language. The retranslation problem is discussed in terms of two criteria-validity and informativeness.

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Section: Computing With Wordsmentioning

confidence: 98%

On the problem of retranslation in computing with perceptions

Martin

Klir

2006

International Journal of General Systems

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“…Since the publication of Zadeh's CW paper, we have witnessed a rapid development of and a fast‐growing interest in this topic and there have been many articles and even books bearing the phrase “computing with words.” A small sample of papers Refs. .…”

Section: Introductionmentioning

confidence: 99%

A general type-2 fuzzy model for computing with words

Jiang

2017

Int. J. Intell. Syst.

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As a methodology, computing with words (CW) allows the use of words, instead of numbers or symbols, in the process of computing and reasoning and thus conforms more to humans’ inference when it is used to describe real‐world problems. In the line of developing a computational theory for CW, in this paper we develop a formal general type‐2 fuzzy model of CW by exploiting general type‐2 fuzzy sets (GT2 FSs) since GT2 FSs bear greater potential to model the linguistic uncertainty. On the one hand, we generalize the interval type‐2 fuzzy sets (IT2 FSs)‐based formal model of CW into general type‐2 fuzzy environments. Concretely, we present two kinds of general type‐2 fuzzy automata (i.e., general type‐2 fuzzy finite automata and general type‐2 fuzzy pushdown automata) as computational models of CW. On the other hand, we also give a somewhat universally general type‐2 fuzzy model of computing with (some special) words and establish a retraction principle from computing with words to computing with values for handling crisp inputs in general type‐2 fuzzy setting and a generalized extension principle from computing with words to computing with all words for handling general type‐2 fuzzy inputs.

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“…Notable works include: [4] where the authors attempt to break away from the traditional rule-based approach to formulate an arithmetic-based technique based on fuzzy numbers; [11] describes the application of the mass assignment theory to fuzzy sets to provide semantic interpretations for membership functions; [18] illustrates the concept of a fuzzy Finite State Machine (FSM) to generate linguistic descriptions of complex phenomenon, which is further extended in [20] to simulate emotions in such a system; [12] explains the relevance of the Interval Type-2 Fuzzy Set (IT2-FS) in mapping the different levels of ambiguities in word perceptions -crucial to the intended human-like responses of a system based on CWW; [17] coalesces the concepts of fuzzy sets and ontology; while [8] and [9] formalize the Generalized Constraint Language (GCL) into a tool for CWW.…”

Section: Introductionmentioning

confidence: 99%

An Insight Into The Z-number Approach To CWW

Pal

Banerjee

Dutta

et al. 2013

Fundamenta Informaticae

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The Z-number is a new fuzzy-theoretic concept, proposed by Zadeh in 2011. It extends the basic philosophy of Computing With Words (CWW) to include the perception of uncertainty of the information conveyed by a natural language statement. The Z-number thus, serves as a model of linguistic summarization of natural language statements, a technique to merge human-affective perspectives with CWW, and consequently can be envisaged to play a radical role in the domain of CWW-based system design and Natural Language Processing (NLP). This article presents a comprehensive investigation of the Z-number approach to CWW. We present here: a) an outline of our understanding of the generic architecture, algorithm and challenges underlying CWW in general; b) a detailed study of the Z-number methodology -where we propose an algorithm for CWW using Z-numbers, define a Z-number based operator for the evaluation of the level of requirement satisfaction, and describe simulation experiments of CWW utilizing Z-numbers; and c) analyse the strengths and the challenges of the Z-numbers, and suggest possible solution strategies. We believe that this article would inspire research on the need for inclusion of human-behavioural aspects into CWW, as well as the integration of CWW and NLP.A system based on CWW, takes as input a set of natural language sentences that describes an event, and presents as output the corresponding response in the natural language as well.The generic architecture for a system that computes with words is hence, as is shown in Figure 1. The components of the architecture are:(i) The Encoder, processes through the two levels of CWW to translate input sentences to the corresponding antecedent constraints, i.e., the inputs are precisiated into some symbolic form (S) as is processed by the system -reminiscent of Russell's concept of philosophical logic;(ii) The Rule Base consists of the antecedent-consequent event relationships, typical to the context of discourse. The rule base may be in the form of an Explanatory Database (ED);(iii) The Inference Engine, in conjunction with the rule base is instrumental in processing the antecedent constraints to arrive at the consequent results. These results are in the symbolic form S';(iv) The Decoder translates S' into words that can be frame semantically correct natural language sentences.

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An arithmetic approach for the computing with words paradigm

Cited by 17 publications

References 9 publications

On the problem of retranslation in computing with perceptions

On the problem of retranslation in computing with perceptions

A general type-2 fuzzy model for computing with words

An Insight Into The Z-number Approach To CWW

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