Dealing with natural language interfaces in a geolocation context

In the geolocation field where high-level programs and low-level devices coexist, it is often difficult to find a friendly user inter- face to configure all the parameters. The challenge addressed in this paper is to propose intuitive and simple, thus natural lan- guage interfaces to interact with low-level devices. Such inter- faces contain natural language processing and fuzzy represen- tations of words that facilitate the elicitation of business-level objectives in our context

Distributed Linguistic Representations in Decision Making: Taxonomy, Key Elements and Applications, and Challenges in Data Science and Explainable Artificial Intelligence

Distributed linguistic representations are powerful tools for modelling the uncertainty and complexity of preference information in linguistic decision making. To provide a comprehensive perspective on the development of distributed linguistic representations in decision making, we present the taxonomy of existing distributed linguistic representations. Then, we review the key elements and applications of distributed linguistic information processing in decision making, including the distance measurement, aggregation methods, distributed linguistic preference relations, and distributed linguistic multiple attribute decision making models. Next, we provide a discussion on ongoing challenges and future research directions from the perspective of data science and explainable artificial intelligence.National Natural Science Foundation of China (NSFC) 71971039 71421001,71910107002,71771037,71874023 71871149Sichuan University sksyl201705 2018hhs-5

Fusion of imprecise qualitative information

In this paper, we present a new 2-tuple linguistic representation model, i.e. Distribution Function Model (DFM), for combining imprecise qualitative information using fusion rules drawn from Dezert-Smarandache Theory (DSmT) framework. Such new approach allows to preserve the precision and efficiency of the combination of linguistic information in the case of either equidistant or unbalanced label model. Some basic operators on imprecise 2-tuple labels are presented together with their extensions for imprecise 2-tuple labels. We also give simple examples to show how precise and imprecise qualitative information can be combined for reasoning under uncertainty. It is concluded that DSmT can deal efficiently with both precise and imprecise quantitative and qualitative beliefs, which extends the scope of this theory

Symbolic Approximate Reasoning Within Unbalanced Multi-sets: Application to Autism Diagnosis

International audienceIn most daily activities, humans often use imprecise information derived from appreciation instead of exact measurements to make decisions. Multisets allow the representation of imperfect information in a Knowledge-Based System (KBS), in the multivalued logic context. New facts are deduced using approximate reasoning. In the literature, dealing with imperfect information relies on an implicit assumption: the distribution of terms is uniform on a scale ranging from 0 to 1. Nevertheless, in some cases, a sub-domain of this scale may be more informative and may include more terms. In this work, we focus on approximate reasoning within these sets, known as unbalanced sets, in the context of multi-valued logic. We introduce an approach based on the Generalized Modus Ponens (GMP) model using Generalized Symbolic Modifiers (GSM). The proposed model is implemented in a tool for autism diagnosis by means of unbalanced severity degrees of the Childhood Autism Rating Scale (CARS). We obtain satisfying results on the distinction between autistic and not autistic child compared to psychiatrists diagnosis

Personalized individual semantics in Computing with Words for supporting linguistic Group Decision Making. An Application on Consensus reaching

Yucheng Dong would like to acknowledge the financial support of grants (Nos. 71171160, 71571124) from NSF of China, and a grant (No.xq15b01) from SSEM key research center at Sichuan province. Enrique Herrera-Viedma and Luis Mart´ınez would like to acknowledge the FEDER funds under Grant TIN2013-40658-P and TIN2015-66524-P respectivelyIn group decision making (GDM) dealing with Computing with Words (CW) has been highlighted the importance of the statement, words mean different things for different people, because of its influence in the final decision. Different proposals that either grouping such different meanings (uncertainty) to provide one representation for all people or use multi-granular linguistic term sets with the semantics of each granularity, have been developed and applied in the specialized literature. Despite these models are quite useful they do not model individually yet the different meanings of each person when he/she elicits linguistic information. Hence, in this paper a personalized individual semantics (PIS) model is proposed to personalize individual semantics by means of an interval numerical scale and the 2-tuple linguistic model. Specifically, a consistency-driven optimization-based model to obtain and represent the PIS is introduced. A new CW framework based on the 2-tuple linguistic model is then defined, such a CW framework allows us to deal with PIS to facilitate CW keeping the idea that words mean different things to different people. In order to justify the feasibility and validity of the PIS model, it is applied to solve linguistic GDM problems with a consensus reaching process.National Natural Science Foundation of China 71171160 71571124Sichuan University skqy201606European Union (EU) TIN2013-40658-P TIN2015-66524-

A Linguistic Group Best–Worst Method for Measuring Good Governance in the Third Sector: A Spanish Case Study

The need of Non-profit Organizations (NPOs) of generating trust and credibility, to their stakeholders by an efficient management of their resources, lead them to openly show that they develop adequate good governance practices. But this is not a simple task and few research has been done on measuring methods of good governance in this field; without achieving an agreement about the best procedure. This paper aims at facilitating the measurement of good governance practices in NPOs by a fuzzy linguistic consensus-based group multi-criteria decision-making (MCGDM) model that will provide agreed and easy-understanding weights for a list of indicators proposed by the stakeholders and entities in such good governance practices. To do that, a linguistic 2-tuple BWM method with a consensus reaching process (CRP) will be developed and then applied to a real-world case in Spain, in which a group of experts from significant Spanish NPOs will assess the list of indicators proposed by the most representative entities (the alliance between the non-governmental organizations (NGO) Platform for Social Action, and the NGO Coordinator for Development (CONGDE) to obtain a prioritization of such indicators for measuring the good governance practices in Spanish NPOs.Ministerio de Economía y Competitividad (Gobierno de España) a través del Proyecto de Investigación Nacional PGC2018-099402-B-I00, la Beca Posdoctoral Ramón y Cajal (RYC-2017-21978) y el proyecto FEDER-UJA 1380637 y ERDF

Managing Incomplete Preference Relations in Decision Making: A Review and Future Trends

In decision making, situations where all experts are able to efficiently express their preferences over all the available options are the exception rather than the rule. Indeed, the above scenario requires all experts to possess a precise or sufficient level of knowledge of the whole problem to tackle, including the ability to discriminate the degree up to which some options are better than others. These assumptions can be seen unrealistic in many decision making situations, especially those involving a large number of alternatives to choose from and/or conflicting and dynamic sources of information. Some methodologies widely adopted in these situations are to discard or to rate more negatively those experts that provide preferences with missing values. However, incomplete information is not equivalent to low quality information, and consequently these methodologies could lead to biased or even bad solutions since useful information might not being taken properly into account in the decision process. Therefore, alternative approaches to manage incomplete preference relations that estimates the missing information in decision making are desirable and possible. This paper presents and analyses methods and processes developed on this area towards the estimation of missing preferences in decision making, and highlights some areas for future research

Dynamic adaptation of user profiles in recommender systems

In a period of time in which the content available through the Internet increases exponentially and is more easily accessible every day, techniques for aiding the selection and extraction of important and personalised information are of vital importance. Recommender Systems (RS) appear as a tool to help the user in a decision making process by evaluating a set of objects or alternatives and aiding the user at choosing which one/s of them suits better his/her interests or preferences. Those preferences need to be accurate enough to produce adequate recommendations and should be updated if the user changes his/her likes or if they are incorrect or incomplete. In this work an adequate model for managing user preferences in a multi-attribute (numerical and categorical) environment is presented to aid at providing recommendations in those kinds of contexts. The evaluation process of the recommender system designed is supported by a new aggregation operator (Unbalanced LOWA) that enables the combination of the information that defines an alternative into a single value, which then is used to rank the whole set of alternatives. After the recommendation has been made, learning processes have been designed to evaluate the user interaction with the system to find out, in a dynamic and unsupervised way, if the user profile in which the recommendation process relies on needs to be updated with new preferences. The work detailed in this document also includes extensive evaluation and testing of all the elements that take part in the recommendation and learning processes