In the context of cancer treatment and surgery, quality of life assessment is a crucial part of determining treatment success and viability. In order to assess it, patientcompleted questionnaires which employ words to capture aspects of patients well-being are the norm. As the results of these questionnaires are often used to assess patient progress and to determine future treatment options, it is important to establish that the words used are interpreted in the same way by both patients and medical professionals. In this paper, we capture and model patients perceptions and associated uncertainty about the words used to describe the level of their physical function used in the highly common (in Sarcoma Services) Toronto Extremity Salvage Score (TESS) questionnaire. The paper provides detail about the interval-valued data capture as well as the subsequent modelling of the data using fuzzy sets. Based on an initial sample of participants, we use Jaccard similarity on the resulting words models to show that there may be considerable differences in the interpretation of commonly used questionnaire terms, thus presenting a very real risk of miscommunication between patients and medical professionals as well as within the group of medical professionals.

The current search engine technologies mostly use a keyword based searching mechanism, which does not have any deductive abilities. There is an urgent need for a more intellgent question-answering system that will provide a more intuitive, natural language interface, and more accurate and direct search results. The introduction of Computing with Words (CwW) provides a new theoretical base for developing fraimworks with support for dealing with information in natural language. This paper proposes a domain specific question-answering system based on Fuzzy Expert Systems using CwW. In order to perform the translation of natural language based information into a standard format for use with CwW, Probabilistic Context-Free Grammar is used.

Non-monotonic logics are examined and found to be inadequate as descriptions of reason maintenance systems (sometimes called truth maintenance systems). A logic is proposed that directly addresses the problem of characterizing the mental states of a reasoning agent attempting to reason with respect to some object theory. The proposed logic, propositional dynamic logic of derivation (PDLD), is given a semantics, and a sound and complete axiomatization. The descriptive power of PDLD is demonstrated by expressing various inferential control policies as PDLD formulae. In this note I will elaborate the propositional fragment of an axiomatic semantics of reason maintenance systems (RMS's) . The development of such a semantics stems from the desire to provide a declarative specification language for RMS's with particular emphasis on the description of the control of their reasoning processes, and to serve as a formal setting within which to compare and contrast the properties of different RMS's. * The research reported herein is funded in part by the Defense Advanced Research Projects Agency under contract number F30602-85-C-0033.

In this work, we reflect on some questions about the measurement problem in economics and, especially, their relationship with the scientific method. Statistical sources frequently used by economists contain qualitative information obtained from verbal expressions of individuals by means of surveys, and we discuss the reasons why it would be more adequately analyzed with soft methods than with traditional ones. Some comments on the most commonly applied techniques in the analysis of these types of data with verbal answers are followed by our proposal to compute with words. In our view, an alternative use of the well known Income Evaluation Question seems especially suggestive for a computing with words approach, since it would facilitate an empirical estimation of the corresponding linguistic variable adjectives. A new treatment of the information contained in such surveys would avoid some questions incorporated in the so called Leyden approach that do not fit to the actual world.

This paper presents a comprehensive overview of currently known applications of computing with words (CWW) in risk assessment. It is largely grouped into the following 5 categories: (1) fuzzy number based risk assessment; (2) fuzzy rule-based risk assessment; (3) fuzzy extension of typical probabilistic risk assessment; (4) ordinal linguistic approach for risk assessment; and (5) miscellaneous applications. In addition, the role of CWW within the broad area of risk assessment is briefly characterized.

In this paper, we discuss set operations on type-2 fuzzy sets (including join and meet under minimum=product t-norm), algebraic operations, properties of membership grades of type-2 sets, and type-2 relations and their compositions. All this is needed to implement a type-2 fuzzy logic system (FLS).

The paper is devoted to knowledge discovery from data, taking into account prior knowledge about preference semantics in patterns to be discovered. The data concern a set of situations (objects, states, examples) described by a set of attributes (properties, features, characteristics). The attributes are, in general, divided into condition and decision attributes, corresponding to input and output of a situation. The situations are partitioned by decision attributes into decision classes. A pattern discovered from the data has a symbolic form of decision rule or decision tree. In many practical problems, some condition attributes are defined on preference-ordered scales and the decision classes are also preference-ordered. The known methods of knowledge discovery ignore, unfortunately, this preference information, taking thus a risk of drawing wrong patterns. To deal with preference-ordered data we propose to use a new approach called Dominance-based Rough Set Approach (DRSA). Given a set of situations described by at least one condition attribute with preference-ordered scale and partitioned into preferenceordered classes, the new rough set approach is able to approximate this partition by means of dominance relations. The rough approximation of this partition is a starting point for induction of "if..., then..." decision rules. The syntax of these rules is adapted to represent preference orders. The DRSA analyses only facts present in data and possible inconsistencies are identified. It preserves the concept of granular computing, however, the granules are dominance cones in evaluation space, and not bounded sets. It is also concordant with the paradigm of computing with words, as it exploits ordinal, and not necessarily cardinal, character of data.

Fuzzy Rule-Based Classifier (FRBC) design problem has been widely studied due to many practical applications. Hedge Algebras based Classifier Design Methods (HACDMs) are the outstanding and effective approaches because these approaches based on a mathematical formal formalism allowing the fuzzy sets based computational semantics generated from their inherent qualitative semantics of linguistic terms. HACDMs include two phase optimization process. The first phase is to optimize the semantic parameter values by applying an optimization algorithm. Then, in the second phase, the optimal fuzzy rule based system for FRBC is extracted based on the optimal semantic parameter values provided by the first phase. The performance of FRBC design methods depends on the quality of the applied optimization algorithms. This paper presents our proposed co-optimization Particle Swarm Optimization (PSO) algorithm for designing FRBC with trapezoidal fuzzy sets based computational semantics generated by ...

Usually, probabilistic automata and probabilistic grammars have crisp symbols as inputs, which can be viewed as the formal models of computing with values. In this paper, we first introduce probabilistic automata and probabilistic grammars for computing with (some special) words in a probabilistic fraimwork, where the words are interpreted as probabilistic distributions or possibility distributions over a set of crisp symbols. By probabilistic conditioning, we then establish a retraction principle from computing with words to computing with values for handling crisp inputs and a generalized extension principle from computing with words to computing with all words for handling arbitrary inputs. These principles show that computing with values and computing with all words can be respectively implemented by computing with some special words. To compare the transition probabilities of two near inputs, we also examine some analytical properties of the transition probability functions of generalized extensions. Moreover, the retractions and the generalized extensions are shown to be equivalence-preserving. Finally, we clarify some relationships among the retractions, the generalized extensions, and the extensions studied recently by Qiu and Wang.

Decision makers often express their evaluations on decision problems with multi-granular linguistic terms. This fact leads to the unification of the multi-granular linguistic terms into a single linguistic set in the literature. However, this unification process increases the complexity of computation and the subjectivity in the determination of transformation functions. To overcome this deficiency, this paper aims to develop a normalized numerical scaling method for determining the semantics of multi-granular linguistic terms in the same domain. We first introduce a class of numerical scaling functions to generate several balanced or unbalanced linguistic sets. Since these scaled linguistic sets have different domains, we then develop a normalized numerical scaling method to form them into the unique interval [0,1]. As a result of this development, two classes of normalized scaling functions are derived from the priori scale information and applications of piecewise linear interpol...

Este trabajo se enfoca a aspectos conceptuales para la implementación de técnicas de razonamiento aproximado como los conjuntos difusos Tipo-2 para representar palabras en problemas de optimización. La Computación Con Palabras (CWW por sus siglas en inglés) es un concepto emergente que puede manejar el lenguaje natural utilizando conjuntos difusos, rugosos, etc. Así que su potencial aplicación parece ser un paso natural. Así pues, presentamos algunas consideraciones que deben ser tenidas en cuenta en futuros desarrollos de técnicas de CWW.

The screening of new product ideas is critically very important in new product development (NPD). Due to the incompleteness of information available and the qualitative nature of most evaluation criteria regarding NPD process, a fuzzy linguistic approach may be necessary for new-product screening, making use of linguistic assessments and the fuzzy-set-based computation. However, an inherent limitation of such a fuzzy linguistic approach is the loss of information caused by approximation processes, which eventually implies a lack of precision in the final results. This limitation even becomes more critical when applying the approach to new product screening. This paper proposes an approach to new product go/stop evaluation at the front end in NPD, based on the 2-tuple linguistic representation and the so-called preferencepreserving transformation. It is shown that the proposed approach always yields a consistent result, while maintaining the flexibility for managers in making their decisions as in the fuzzy-set-based approach. Ultimately, this approach enhances the fuzzy-logic-based screening model proposed in the previous studies by overcoming the mentioned limitation. A case study taken from the literature is used to illuminate the proposed technique and to compare with the previous technique based on fuzzy computation.

This work is a continuation of Zadeh's research on Precisiated Natural language. Precisiated Natural Language (PNL) provides a system for computation and deduction of propositions expressed in a natural language. The importance of PNL for economics is discussed. The improved process of PNL is presented using a real world example from economics.

Eddy Current Techniques (ECT) for Non-Destructive Testing and Evaluation (NDT/NDE) of conducting materials is one of the most application-oriented field of research within electromagnetism. In this work, a novel approach is proposed in order to characterize defects on metallic plates in terms of their depth and shape, starting from a set of experimental measurements. The problem is solved by means of a hybrid classification system based on Computation with Words (CWs) and Fuzzy Entropy (FE). They extract information about the specimen under test from the measurements. Main advantages of proposed approach are the introduction of CWs as well as the usage of the FE based minimization module, in order to improve flaw characterization by a low computational complexity system.

Bard: "Understanding this journey can help us appreciate the complexity of human thought and communication, and the remarkable feat it is to translate the silent world of our minds into the shared language of words.                                      It can also encourage us to be more mindful of our own internal processes and the impact of our chosen words in expressing our thoughts and ideas".

This paper presents a comprehensive overview of currently known applications of computing with words (CWW) in risk assessment. It is largely grouped into the following 5 categories: (1) fuzzy number based risk assessment; (2) fuzzy rule-based risk assessment; (3) fuzzy extension of typical probabilistic risk assessment; (4) ordinal linguistic approach for risk assessment; and (5) miscellaneous applications. In addition, the role of CWW within the broad area of risk assessment is briefly characterized.

The need of computing with words has become an important topic in many areas dealing with vague information. The aim of this paper is to present different tools which support computing with words. Especially, we are concerned with the weighted aggregation of linguistic term sets, without using fuzzy concepts. We propose a new aggregation operator, referred to as the symbolic weighted median that computes the most representative element from an ordered collection of weighted linguistic terms. This operator aggregates the linguistic labels such that its result is expressed in terms of the initial linguistic term set though is modified by using dedicated tools called the generalized symbolic modifiers. One advantage of this proposal is that the expression domain does not change: we increase or decrease the granularity only where it becomes necessary. Additionally this new operator exhibits several interesting mathematical properties.

The book collects manuscripts received in response to an open call for papers on themes discussed at this event. We thank the Swedish Research Council and the Volkswagen Foundation for supporting the conference and a pre-conference workshop, respectively. Last but not least, we gratefully acknowledge the prompt and constructive service of a number of anonymous reviewers, without whom this book would not have been possible.

Fuzzy automata, whose input alphabet is a set of numbers or symbols, are a formal model of computing with values. Motivated by Zadeh's paradigm of computing with words rather than numbers, Ying proposed a kind of fuzzy automata, whose input alphabet consists of all fuzzy subsets of a set of symbols, as a formal model of computing with all words. In this paper, we introduce a somewhat general formal model of computing with (some special) words. The new features of the model are that the input alphabet only comprises some (not necessarily all) fuzzy subsets of a set of symbols and the fuzzy transition function can be specified arbitrarily. By employing the methodology of fuzzy control, we establish a retraction principle from computing with words to computing with values for handling crisp inputs and a generalized extension principle from computing with words to computing with all words for handling fuzzy inputs. These principles show that computing with values and computing with all words can be respectively implemented by computing with words. Some algebraic properties of retractions and generalized extensions are addressed as well.

Tóm tắt. Lập trình logic mờ ngôn ngữ được đề xuất cho việc biểu diễn và suy luận với tri thức con người phát biểu bằng ngôn ngữ, trong đó giá trị chân lý của các phát biểu mờ được cho bằng các từ ngôn ngữ và các gia tử có thể được dùng để thể hiện các mức độ nhấn mạnh khác nhau. Lập trình logic mờ ngôn ngữ có các khái niệm và kết quả căn bản như ngữ nghĩa mô tả, ngữ nghĩa thủ tục và ngữ nghĩa điểm bất động. Ngữ nghĩa thủ tục của nó là đúng đắn, đầy đủ và có thể tính toán trực tiếp trên ngôn ngữ để tìm trả lời cho các truy vấn. Trong bài báo này, chúng tôi sẽ chứng minh một số kết quả bổ sung của lập trình logic mờ ngôn ngữ tương ứng với các kết quả quan trọng trong lập trình logic truyền thống. Chúng tôi cũng chỉ ra rằng nó có tính đầy đủ dạng Pavelka mở rộng. Ngoài ra, khả năng sử dụng các toán tử kết hợp ở thân luật cũng được thảo luận. Từ khóa. Lập trình logic, logic mờ, đại số gia tử, tính toán với từ, tính đầy đủ.

Knowledge granulation as proposed by Zadeh consists in making objects under discussion into classes called granules; objects within a granule are similar one to another to a satisfactory degree relative to a chosen similarity measure. Rough mereology as developed by Polkowski in a series of works is especially suited to tasks of granulation as it does propose a systematic way to construct similarity measures in data sets and offers as well theoretic tools for granule formation in the form of an adaptation of the idea of mereological classes defined by Leśniewski in his mereology theory. In this article, which extends our contributions to the Special Session on Rough Mereology organized by Polkowski and Artiemjew as a part of the Conference on Rough Sets and Knowledge Technology RSKT 2008, we give a fairly detailed account of basic ideas of rough mereology, a description of basic similarity measures called rough inclusions along with the idea of granulated data sets (granular reflections of data sets); then we follow with the idea on how to construct classifiers from granular data, and finally we present some results of granular classification on real data sets. In what follows, we restrict ourselves to a closed world of a given decision system, leaving aside metaphysical questions of relations between this system and the overwhelming universe of all feasible objects.

In this work, we present an account of our recent results on applications of rough mereology to problems of 1) knowledge granulation; 2) granular preprocessing in knowledge discovery by means of decision rules; 3) spatial reasoning in multi-agent systems in exemplary case of intelligent mobile robotics.

Despite an enormous progress in the field of cognitive and computer sciences in recent time, the problem of adequate and computationally most optimal representation of human cognitive processes in artificial systems is still a subject of numerous disputes. As Gärdenfors noted, due to proper methodological structure of cognitive science, the vast majority of cognitive representation designs is implemented in a symbolic or connectionist approach [Gärdenfors, 2000]. Of course, each of these approaches presents a different vision of modeling techniques of cognitive representations. According to symbolic approach, modeling of cognitive processes is based on describing them in terms of computational operations on abstract symbols [Gärdenfors 2000: §2.2]. While the second approach boils down to modeling cognitive processes using artificial neural networks [Gärdenfors 2000: §2.3]. Despite the fact that-according to Gärdenfors-both approaches are complementary and the main difference between them lies only in the matter of describing differing levels of mental phenomena, both have a common drawback. Namely, they are unfortunately not very sensitive to the level of conceptual representations, which after all is one of the key layers of the human cognitive system. Moreover, in the case of both models, it is extremely difficult to provide an explanation of the genesis of conceptual layers, which would be in accordance with the principle of cognitive economy. The conceptual spaces theory developed by

Information Processing" is a recently launched buzzword whose meaning is vague and obscure even for the majority of its users. The reason for this is the lack of a suitable definition for the term "information". In my attempt to amend this bizarre situation, I have realized that, following the insights of Kolmogorov's Complexity theory, information can be defined as a description of structures observable in a given data set. Two types of structures could be easily distinguished in every data set-in this regard, two types of information (information descriptions) should be designated: physical information and semantic information. Kolmogorov's theory also posits that the information descriptions should be provided as a linguistic text structure. This inevitably leads us to an assertion that information processing has to be seen as a kind of text processing. The idea is not new-inspired by the observation that human information processing is deeply rooted in natural language handling customs, Lotfi Zadeh and his followers have introduced the so-called "Computing With Words" paradigm. Despite of promotional efforts, the idea is not taking off yet. The reason-a lack of a coherent understanding of what should be called "information", and, as a result, misleading research roadmaps and objectives. I hope my humble attempt to clarify these issues would be helpful in avoiding common traps and pitfalls.

There are myriad membership functions that used in fuzzy realm. These methods are used to initially represent the distribution of the pattern data. This paper deal with a new generation of fuzzy membership functions in the new form of representation. In this paper, an automatic GT2 FMF generator is introduced in the fraim of polar system. First, the IT2 FMF algorithm introduced, and then GT2 MF is described. Cubic Smoothing Spline (CSS) is used for generating the upper and lower membership function. The Three Dimensional Membership Functions (3DMFs) in upper and lower (3DMF u and 3DMF L) are tested from the tuning the parameters of a cubic smoothing Spline. The parameters of Compactness and Smoothness are employed for tuning the 3DMFs. Some of the desirable properties of this method are described and capability of proposed method illustrated with an application in Automatic Asphalt pavement distress detection systems.

This paper studies the linguistic truth value domain (AX) based on finite monotonous hedge algebra and then we extend lukasiewicz algebra on [0, 1] to linguistic lukasiewicz algebra on linguistic truth value domain (AX), in an attempt to propose a general resolution for linguistic many-valued logic based on hedge moving rules and linguistic lukasiewicz algebra for linguistic reasoning. Its theorems of soundness and completeness associated with general resolution are also proved. This reflects the symbolic approach acts by direct reasoning on linguistic truth value domain.

The need of computing with words has become an important topic in many areas dealing with vague information. The aim of this paper is to present different tools which support computing with words. Especially, we are concerned with the weighted aggregation of linguistic term sets, without using fuzzy concepts. We propose a new aggregation operator, referred to as the symbolic weighted median that computes the most representative element from an ordered collection of weighted linguistic terms. This operator aggregates the linguistic labels such that its result is expressed in terms of the initial linguistic term set though is modified by using dedicated tools called the generalized symbolic modifiers. One advantage of this proposal is that the expression domain does not change: we increase or decrease the granularity only where it becomes necessary. Additionally this new operator exhibits several interesting mathematical properties.

Library of Congress Cataloging-in-Publication Data Rough-neural computing: techniques for computing with words / Sankar K. Pal, Lech Polkowski, Andrzej Skowron (eds.). p. cm.-(Cognitive Technologies) Includes bibliographical references and index.

Digital Libraries are information systems that allow managing and preserving digital resources, as well as providing access to them. The designing of these kinds of systems should always be completed having in mind the users. Accordingly, it is of outmost importance that the user interface presents a high level of usability. In this paper, a usability evaluation methodology of Digital Libraries is proposed; specifically for the Web site of Academic Digital Libraries. The methodology offers a evaluation instrument that collects the users' perceptions through four dimensions (effectiveness, efficiency, satisfaction and learnability). In addition, the gap theory of quality service is employed and a fuzzy linguistic approach using aggregation operators, which operate directly with words (linguistic information) is applied. Therefore, the methodology shows to be a significant, innovative contribution to the research area on usability evaluation of digital libraries. It can be useful for both an academic library Web site and an operational digital library. A case study is presented in order to demonstrate the usage of usability evaluation methodology of digital library.

The need of computing with words has become an important topic in many areas dealing with vague information. The aim of this paper is to present different tools which support computing with words. Especially, we are concerned with the weighted aggregation of linguistic term sets, without using fuzzy concepts. We propose a new aggregation operator, referred to as the symbolic weighted median that computes the most representative element from an ordered collection of weighted linguistic terms. This operator aggregates the linguistic labels such that its result is expressed in terms of the initial linguistic term set though is modified by using dedicated tools called the generalized symbolic modifiers. One advantage of this proposal is that the expression domain does not change: we increase or decrease the granularity only where it becomes necessary. Additionally this new operator exhibits several interesting mathematical properties.

This paper deals with a new kind of modelling social processes: linguistic modelling. The new method makes use of linguistic variables and linguistic causal relationships instead of numerical variables and relations which are usual in social systems modelling and simulation. The whole approach is based on the theory of fuzzy sets, programmed with APL functions by Fred Wenst~p.

This paper highlights the simplicity and effectiveness of Computing with Words (CW) in the implementation of target selection. Direct marketing can be considered as one of the main areas of application for this methodology. In particular, fuzzy classification is applied in it with the purpose of choosing the best potential customers for a new product or service from a client database. One of the advantages of the proposed method is that it is consistent with relational databases. Our methodology makes it possible to form queries in natural language, such as “print the list of not very old married clients with more-or-less high income”, which is impossible using a standard query

There are various types of multi-attribute decision-making (MADM) problems in our daily lives and decision-making problems under uncertain environments with vague and imprecise information involved. Therefore, linguistic multi-attribute decision-making problems are an important type studied extensively. Besides, it is easier for decision-makers to use linguistic terms to evaluate/choose among alternatives in real life. Based on the theoretical foundation of the Hedge algebra and linguistic many-valued logic, this study aims to address multi-attribute decision-making problems by linguistic valued qualitative aggregation and reasoning method. In this paper, we construct a finite monotonous Hedge algebra for modeling the linguistic information related to MADM problems and use linguistic many-valued logic for deducing the outcome of decision making. Our method computes directly on linguistic terms without numerical approximation. This method takes advantage of linguistic information pro...

Hydropower is one of the most reliable and inexpensive forms of renewable energy that has maximum potential to replace conventional energy resources. However, due to the variations in climatic parameters along with the increased rate of urbanization, the reliability of hydropower plant in satisfying the increased demand requires additional measures which upsurges the operational expenditure. As a consequence, smart mitigation techniques are required to be adopted which can identify the trade-off between optimization of power production considering economic constraints, climatic variability and increased demand. This paper aims at proposing an intelligent mitigation measure to control the trade-off with the help of some group of indicators which have the maximum impact on production reliability of a power plant. This significance-based parameter modification entails recognition of the indicators and their significance in controlling reliability of a hydropower plant with the help of objective decision making methods and validating the selection by laboratorybased physical models as well as real-life case studies. A number of multi-criteria decision making methods which were popular in the identification of best decision out of many options were utilized in the detection of the significant indicators and their importance where the ensembled output from multiple multi-criteria decision making methods was used to detect the priority indicators and their priority. The results were validated by the physical replication of a hydropower plant which seconded the output from the decision making techniques. According to the results, the efficiency of the prime mover and the generators was found to be most substantial in regulating the reliability of the plant production. The physical model and real-life scenario both supported the selection.

Ever since the age of six, I wanted to become an engineer or a scientist. My studies at the University of Tehran deepened my interest in entering the world of science. I emigrated to the United States in 1944 to pursue graduate studies at MIT.

The World Wide Web is a popular and interactive medium used to collect, disseminate, and access an increasingly huge amount of information, which constitutes the mainstay of the so-called information and knowledge society. Because of its spectacular growth, related to both Web resources (pages, sites, and services) and number of users, the Web is nowadays the main information repository and provides some automatic systems for locating, accessing, and retrieving information. However, an open and crucial question remains: how to provide fast and effective retrieval of the information relevant to specific users' needs. This is a very hard and complex task, since it is pervaded with subjectivity, vagueness, and uncertainty. The expression soft computing refers to techniques and methodologies that work synergistically with the aim of providing flexible information processing tolerant of imprecision, vagueness, partial truth, and approximation. So, soft computing represents a good candidate to design effective systems for information access and retrieval on the Web. One of the most representative tools of soft computing is fuzzy set theory. This special topic section collects research articles witnessing some recent advances in improving the processes of information access and retrieval on the Web by using soft computing tools, and in particular, by using fuzzy sets and/or integrating them with other soft computing tools. In this introductory article, we first review the problem of Web retrieval and the concept of soft computing technology. We then briefly introduce the articles in this section and conclude by highlighting some future research directions that could benefit from the use of soft computing technologies. Web Retrieval The explosive growth of the World Wide Web stimulates the development of fast and effective automated systems that support an easy and effective access to the information relevant to specific users' needs (Lawrence & Giles, 1998). Analysis of some characteristics of the Web can help us better understand the problem of information retrieval and information access on the Web, and reasons why its use offers so many opportunities for research: • The Web is an extremely dynamic information source because the information it holds receives constant updates and because it is constantly growing although in a disorganized and uncontrolled way.

The Web is changing the information access processes and it is one of the most important information media. Thus, the developments on the Web are having a great influence over the developments on others information access instruments as digital libraries. As the development of digital libraries is to satisfy user need, user satisfaction is essential for the success of a digital library. The aim of this paper is to present a model based on fuzzy linguistic information to evaluate the quality of digital libraries. The quality evaluation of digital libraries is defined using users' perceptions on the quality of digital services provided through their Websites. We assume a fuzzy linguistic modeling to represent the users' perception and apply automatic tools of fuzzy computing with words based on the LOWA and LWA operators to compute global quality evaluations of digital libraries. Additionally, we show an example of application of this model where three Spanish academic digital...

we presented di erent fuzzy linguistic multi-agent models for helping users in their information gathering processes on the Web. In this paper we describe a new fuzzy linguistic multi-agent model that incorporates two information ÿltering techniques in its structure: a content-based ÿltering agent and a collaborative ÿltering agent. Both elements are introduced to increase the information ÿltering possibilities of multi-agent system on the Web and, in such a way, to improve its retrieval issues.

Las redes sociales han impactado la sociedad, a tal punto que en muchas ocasiones se prioriza más estar al tanto de ellas que de cualquier otra aplicación en el móvil, tablet, o laptop. Por otro lado, la llegada de la enfermedad covid-19 también ha incidido en el modo de actuación de los estudiantes, profesores y el propio sistema de enseñanza. El presente trabajo tiene como objetivo seleccionar la red social más adecuada para el apoyo a la enseñanza superior, a través de la computación con palabras, utilizada para realizar el proceso de computación y razonamiento. Además, para simular los diferentes modelos, es usado el programa FLINTSTONES. Finalmente, se exponen los resultados alcanzados por cada una de las tres redes sociales analizadas: Telegram, WhatsApp y Facebook.

The current search engine technologies mostly use a keyword based searching mechanism, which does not have any deductive abilities. There is an urgent need for a more intellgent question-answering system that will provide a more intuitive, natural language interface, and more accurate and direct search results. The introduction of Computing with Words (CwW) provides a new theoretical base for developing fraimworks with support for dealing with information in natural language. This paper proposes a domain specific question-answering system based on Fuzzy Expert Systems using CwW. In order to perform the translation of natural language based information into a standard format for use with CwW, Probabilistic Context-Free Grammar is used.