An API for generalized constraint language based expert system

Patel, Purvag; Khorasani, Elham S.; Rahimi, Shahram

doi:10.1109/nafips.2012.6290997

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…This design was chosen considering that the fuzzy sets in our toolkit are modeled with discrete values and the probability value for a continuous probability distribution at a single point in space is always zero. This method has proved beneficial in implementing various complex constructs in previous release of CWShell [7][6] [8].…”

Section: A Discretization Of Probability Distributionmentioning

confidence: 99%

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Applied Z-numbers

Patel¹,

Rahimi²,

Khorasani

2015

2015 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS) Held Jointly With 2015 5th World Con

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Z-number is an emerging paradigm that has been utilized in computing with words among others. The concept of a Z-number is intended to provide a basis for computation with numbers that deal with reliability and likelihood. Z-numbers are confluence between the two most prominent approaches to uncertainty, probability and possibility, which allow computations on complex statements. computations on Z-numbers require solving a complex optimization problem over the space of all possible probability distribution which leads in to its slow adoption to computing with words machinery. This paper seeks to provide an applied model of Z-numbers based on certain realistic assumptions regarding the probability distributions. An algorithm and example is presented to demonstrate the applicability of the model.

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Section: A Discretization Of Probability Distributionmentioning

confidence: 99%

“…The goal of this research is to present a general solution that can be applied to a wide variety of problem definitions involving fuzzy probabilistic statements and also integrate this solution into CWShell. CWShell is an inference engine for computing with words; its implementation is detailed in our previous works [6][7] [8].…”

Section: Introductionmentioning

confidence: 99%

Applied Z-numbers

Patel¹,

Rahimi²,

Khorasani

2015

2015 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS) Held Jointly With 2015 5th World Con

Self Cite

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“…Nevertheless, CWW has witnessed a resurrection through its application to various real-world problems including analysis of complex systems [102], control [59,156], risk assessment [54], decision-making [21,31,35,36,40,62,65,115], classification [1], website quality assessment [37], reinforcement learning [157], text processing [155], object oriented programming [5], information retrieval [4,48], expert systems [45,46,77,97], reputation systems [131], and natural language generation [41].…”

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confidence: 99%

Advanced Computing with Words: Status and Challenges

Mendel

Rajati

2015

Towards the Future of Fuzzy Logic

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In this chapter, we focus on the status of Advanced Computing with Words (ACWW) and the challenges that it may encounter in the future. First, we elaborate on the notion of Computing with Words (CWW) and its various subareas. Then we present some non-engineering ACWW problems and connect them to more realistic engineering problems, after which we provide a roadmap for solving ACWW problems, and show how the Generalized Extension Principle (GEP) can be used to formulate their solutions. We also propose a syllogistic approach to solving ACWW problems that also uses the Extension Principle but in a different way. Finally, we discuss present and future challenges to ACWW, i.e. we explain what challenges ACWW encounters given the current trend of data abundance and widespread use of Internet for dealing with questions posed in natural languages. Introduction: Computing with Words and Advanced Computing with WordsComputing with Words (CWW or CW) [11,29,34,66,69,70,75,91,104,109,142,144,147,154] was probably conceived as the main area of application of fuzzy logic [132] when the field was originated. It is a methodology of computation whose objects are words rather than numbers, although those words are linked to numbers and classical calculations that can be carried out by computing machinery, via membership functions of fuzzy sets associated with words. To do that, a process called precisiation of meaning [152] is essential, which, in a nutshell, is determining membership functions of the fuzzy sets that model the words. Zadeh distinguishes two subareas for CWW:1. Basic Computing with Words (BCWW), which mainly deals with descriptions of complex systems in terms of fuzzy IF-THEN rules [134]. Soft constraints are generally possibilistic, i.e. they describe (physical) attributes like speed, temperature,

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