224 research outputs found
Intelligent computational argumentation for evaluating performance scores in multi-criteria decision making
Multi Criteria Decision Making (MCDM) is a discipline aimed at assisting multiple stakeholders in contemplating a decision paradigm in an uncertain environment. The decision analysis to be performed involves numerous alternative positions assessed under varied criterion. A performance score is assigned for each alternative in terms of every criterion and it represents satisfaction of the criteria by that alternative. In a collaborative decision making environment, performance scores are either obtained when a consensus can be reached among stakeholders on a particular score or in some cases or controversial when stakeholders do not agree with each other about them. In the previous research an intelligent argumentation system for collaborative decision making was developed. In this thesis; its use is being extended for evaluating performance scores in MCDM. A framework is laid out for using the Intelligent Argumentation approach for resolving controversial performance scores. An application case study of Selection of a Mine Detection Simulation tool is used to illustrate the method. To validate it empirically, a case study to determine division of effort between software quality assurance and software testing, which has a group of 24 stakeholders, is conducted in a hypothetical setup. Its empirical data is collected and analyzed. The analysis serves two basic purposes: 1) to validate capability of the argumentation process in determining the controversial performance scores in MCDM using our intelligent computational argumentation system and to show its effectiveness in capturing rationales of stakeholders and assisting rapid collaborative decision making --Abstract, page iii
Literature review and discussion on collaborative decision making approaches in industry 4.0
Nowadays, companies are faced with an increasingly higher level of competition while trying to adapt to the exigencies imposed by the Industry 4.0, regarding its usually referred dimensions and pillars, among which one that although is not so often referred is also expressing an increasing visibility and importance, related to collaboration, and more specifically to collaborative decision making and co-working. Thus, in this paper an analysis is carried out regarding the evolution of publications that have been put available over the last decade about collaborative decision making approaches, varying from approaches based on mathematical models up to the application of artificial intelligence and other kind of approaches. Moreover, a discussion about the relation between collaborative decision making, concurrent engineering and Industry 4.0 dimensions is also done.This work has been supported by FCT -Fundacao para a Ciencia e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020
Contributions to the selection and implementation of standard software for CRM and electronic invoicing
[no abstract
A decade of application of the Choquet and Sugeno integrals in multi-criteria decision aid
The main advances regarding the use of the Choquet and Sugeno integrals in multi-criteria decision aid over the last decade are reviewed. They concern mainly a bipolar extension of both the Choquet integral and the Sugeno integral, interesting particular submodels, new learning techniques, a better interpretation of the models and a better use of the Choquet integral in multi-criteria decision aid. Parallel to these theoretical works, the Choquet integral has been applied to many new fields, and several softwares and libraries dedicated to this model have been developed.Choquet integral, Sugeno integral, capacity, bipolarity, preferences
Argumentation Stance Polarity and Intensity Prediction and its Application for Argumentation Polarization Modeling and Diverse Social Connection Recommendation
Cyber argumentation platforms implement theoretical argumentation structures that promote higher quality argumentation and allow for informative analysis of the discussions. Dr. Liu’s research group has designed and implemented a unique platform called the Intelligent Cyber Argumentation System (ICAS). ICAS structures its discussions into a weighted cyber argumentation graph, which describes the relationships between the different users, their posts in a discussion, the discussion topic, and the various subtopics in a discussion. This platform is unique as it encodes online discussions into weighted cyber argumentation graphs based on the user’s stances toward one another’s arguments and ideas. The resulting weighted cyber argumentation graphs can then be used by various analytical models to measure aspects of the discussion. In prior work, many aspects of cyber argumentation have been modeled and analyzed using these stance relationships.
However, many challenging problems remain in cyber argumentation. In this dissertation, I address three of these problems: 1) modeling and measure argumentation polarization in cyber argumentation discussions, 2) encouraging diverse social networks and preventing echo chambers by injecting ideological diversity into social connection recommendations, and 3) developing a predictive model to predict the stance polarity and intensity relationships between posts in online discussions, allowing discussions from outside of the ICAS platform to be encoded as weighted cyber argumentation graphs and be analyzed by the cyber argumentation models. In this dissertation, I present models to measure polarization in online argumentation discussions, prevent polarizing echo-chambers and diversifying users’ social networks ideologically, and allow online discussions from outside of the ICAS environment to be analyzed using the previous models from this dissertation and the prior work from various researchers on the ICAS system.
This work serves to progress the field of cyber argumentation by introducing a new analytical model for measuring argumentation polarization and developing a novel method of encouraging ideological diversity into social connection recommendations. The argumentation polarization model is the first of its kind to look specifically at the polarization among the users contained within a single discussion in cyber argumentation. Likewise, the diversity enhanced social connection recommendation re-ranking method is also the first of its kind to introduce ideological diversity into social connections. The former model will allow stakeholders and moderators to monitor and respond to argumentation polarization detected in online discussions in cyber argumentation. The latter method will help prevent network-level social polarization by encouraging social connections among users who differ in terms of ideological beliefs. This work also serves as an initial step to expanding cyber argumentation research into the broader online deliberation field. The stance polarity and intensity prediction model presented in this dissertation is the first step in allowing discussions from various online platforms to be encoded into weighted cyber argumentation graphs by predicting the stance weights between users’ posts. These resulting predicted weighted cyber augmentation graphs could then be used to apply cyber argumentation models and methods to these online discussions from popular online discussion platforms, such as Twitter and Reddit, opening many new possibilities for cyber argumentation research in the future
Polarization and opinion analysis in an online argumentation system for collaborative decision support
Argumentation is an important process in a collaborative decision making environment. Argumentation from a large number of stakeholders often produces a large argumentation tree. It is challenging to comprehend such an argumentation tree without intelligent analysis tools. Also, limited decision support is provided for its analysis by the existing argumentation systems. In an argumentation process, stakeholders tend to polarize on their opinions, and form polarization groups. Each group is usually led by a group leader. Polarization groups often overlap and a stakeholder is a member of multiple polarization groups. Identifying polarization groups and quantifying a stakeholder\u27s degree of membership in multiple polarization groups helps the decision maker understand both the social dynamics and the post-decision effects on each group.
Frameworks are developed in this dissertation to identify both polarization groups and quantify a stakeholder\u27s degree of membership in multiple polarization groups. These tasks are performed by quantifying opinions of stakeholders using argumentation reduction fuzzy inference system and further clustering opinions based on K-means and Fuzzy c-means algorithms.
Assessing the collective opinion of the group on individual arguments is also important. This helps stakeholders understand individual arguments from the collective perspective of the group. A framework is developed to derive the collective assessment score of individual arguments in a tree using the argumentation reduction inference system. Further, these arguments are clustered using argument strength and collective assessment score to identify clusters of arguments with collective support and collective attack.
Identifying outlier opinions in an argumentation tree helps in understanding opinions that are further away from the mean group opinion in the opinion space. Outlier opinions may exist from two perspectives in argumentation: individual viewpoint and collective viewpoint of the group. A framework is developed in this dissertation to address this challenge from both perspectives.
Evaluation of the methods is also presented and it shows that the proposed methods are effective in identifying polarization groups and outlier opinions. The information produced by these methods help decision makers and stakeholders in making more informed decisions --Abstract, pages iii-iv
Aplicação de técnicas de Clustering ao contexto da Tomada de Decisão em Grupo
Nowadays, decisions made by executives and managers are primarily made in a group. Therefore, group decision-making is a process where a group of people called participants work together to analyze a set of variables, considering and evaluating a set of alternatives to select one or more solutions. There are many problems associated with group decision-making, namely when the participants cannot meet for any reason, ranging from schedule incompatibility to being in different countries with different time zones. To support this process, Group Decision Support Systems (GDSS) evolved to what today we call web-based GDSS. In GDSS, argumentation is ideal since it makes it easier to use justifications and explanations in interactions between decision-makers so they can sustain their opinions. Aspect Based Sentiment Analysis (ABSA) is a subfield of Argument Mining closely related to Natural Language Processing. It intends to classify opinions at the aspect level and identify the elements of an opinion. Applying ABSA techniques to Group Decision Making Context results in the automatic identification of alternatives and criteria, for example. This automatic identification is essential to reduce the time decision-makers take to step themselves up on Group Decision Support Systems and offer them various insights and knowledge on the discussion they are participants. One of these insights can be arguments getting used by the decision-makers about an alternative. Therefore, this dissertation proposes a methodology that uses an unsupervised technique, Clustering, and aims to segment the participants of a discussion based on arguments used so it can produce knowledge from the current information in the GDSS. This methodology can be hosted in a web service that follows a micro-service architecture and utilizes Data Preprocessing and Intra-sentence Segmentation in addition to Clustering to achieve the objectives of the dissertation. Word Embedding is needed when we apply clustering techniques to natural language text to transform the natural language text into vectors usable by the clustering techniques. In addition to Word Embedding, Dimensionality Reduction techniques were tested to improve the results. Maintaining the same Preprocessing steps and varying the chosen Clustering techniques, Word Embedders, and Dimensionality Reduction techniques came up with the best approach. This approach consisted of the KMeans++ clustering technique, using SBERT as the word embedder with UMAP dimensionality reduction, reducing the number of dimensions to 2. This experiment achieved a Silhouette Score of 0.63 with 8 clusters on the baseball dataset, which wielded good cluster results based on their manual review and Wordclouds. The same approach obtained a Silhouette Score of 0.59 with 16 clusters on the car brand dataset, which we used as an approach validation dataset.Atualmente, as decisões tomadas por gestores e executivos são maioritariamente realizadas em grupo. Sendo assim, a tomada de decisão em grupo é um processo no qual um grupo de pessoas denominadas de participantes, atuam em conjunto, analisando um conjunto de variáveis, considerando e avaliando um conjunto de alternativas com o objetivo de selecionar uma ou mais soluções. Existem muitos problemas associados ao processo de tomada de decisão, principalmente quando os participantes não têm possibilidades de se reunirem (Exs.: Os participantes encontramse em diferentes locais, os países onde estão têm fusos horários diferentes, incompatibilidades de agenda, etc.). Para suportar este processo de tomada de decisão, os Sistemas de Apoio à Tomada de Decisão em Grupo (SADG) evoluíram para o que hoje se chamam de Sistemas de Apoio à Tomada de Decisão em Grupo baseados na Web. Num SADG, argumentação é ideal pois facilita a utilização de justificações e explicações nas interações entre decisores para que possam suster as suas opiniões. Aspect Based Sentiment Analysis (ABSA) é uma área de Argument Mining correlacionada com o Processamento de Linguagem Natural. Esta área pretende classificar opiniões ao nível do aspeto da frase e identificar os elementos de uma opinião. Aplicando técnicas de ABSA à Tomada de Decisão em Grupo resulta na identificação automática de alternativas e critérios por exemplo. Esta identificação automática é essencial para reduzir o tempo que os decisores gastam a customizarem-se no SADG e oferece aos mesmos conhecimento e entendimentos sobre a discussão ao qual participam. Um destes entendimentos pode ser os argumentos a serem usados pelos decisores sobre uma alternativa. Assim, esta dissertação propõe uma metodologia que utiliza uma técnica não-supervisionada, Clustering, com o objetivo de segmentar os participantes de uma discussão com base nos argumentos usados pelos mesmos de modo a produzir conhecimento com a informação atual no SADG. Esta metodologia pode ser colocada num serviço web que segue a arquitetura micro serviços e utiliza Preprocessamento de Dados e Segmentação Intra Frase em conjunto com o Clustering para atingir os objetivos desta dissertação. Word Embedding também é necessário para aplicar técnicas de Clustering a texto em linguagem natural para transformar o texto em vetores que possam ser usados pelas técnicas de Clustering. Também Técnicas de Redução de Dimensionalidade também foram testadas de modo a melhorar os resultados. Mantendo os passos de Preprocessamento e variando as técnicas de Clustering, Word Embedder e as técnicas de Redução de Dimensionalidade de modo a encontrar a melhor abordagem. Essa abordagem consiste na utilização da técnica de Clustering KMeans++ com o SBERT como Word Embedder e UMAP como a técnica de redução de dimensionalidade, reduzindo as dimensões iniciais para duas. Esta experiência obteve um Silhouette Score de 0.63 com 8 clusters no dataset de baseball, que resultou em bons resultados de cluster com base na sua revisão manual e visualização dos WordClouds. A mesma abordagem obteve um Silhouette Score de 0.59 com 16 clusters no dataset das marcas de carros, ao qual usamos esse dataset com validação de abordagem
The effectiveness of virtual facilitation in supporting GDSS appropriation and structured group decision making
Since their introduction a quarter of a century ago, group decision support systems (GDSS) have evolved from applications designed primarily to support decision making for groups in face-to-face settings, to their growing use for “web conferencing,” online collaboration, and distributed group decision-making. Indeed, it is only recently that such groupware applications for conducting face-to-face, as well as “virtual meetings” among dispersed workgroups have achieved mainstream status, as evidenced by Microsoft’s ubiquitous advertising campaign promoting its “Live Meeting” electronic meeting systems (EMS) software. As these applications become more widely adopted, issues relating to their effective utilization are becoming increasingly relevant. This research addresses an area of growing interest in the study of group decision support systems, and one which holds promise for improving the effective utilization of advanced information technologies in general: the feasibility of using virtual facilitation (system-directed multi-modal user support) for supporting the GDSS appropriation process and for improving structured group decision-making efficiency and effectiveness. A multi-modal application for automating the GDSS facilitation process is used to compare conventional GDSS-supported groups with groups using virtual facilitation, as well as groups interacting without computerized decision-making support. A hidden-profile task designed to compare GDSS appropriation levels, user satisfaction, and decision-making efficiency and effectiveness is utilized in an experiment employing auditors, accountants, and IT security professionals as participants. The results of the experiment are analyzed and possible directions for future research efforts are discussed
Organizational maturity for co-creation: Towards a multi-attribute decision support model for public organizations
The paper conceptualizes a multi-attribute decision support model for the assessment of organizational maturity for co-creation, specifically for public organizations. This is achieved on the basis of a systematic literature review (i.e. content analysis) and analysis of two European case studies of promising collaborative practices (from UK and Slovenia). The co-creation drivers and barriers elicited from these two sources are integrated in a decision support model, thus setting the layout of a multi-attribute decision support model for the assessment of organizational maturity for co-creation. The final model conceptualized here consists of 25 attributes or criteria grouped into three categories: organization capacity, staff capacity, and a wider political and normative context in which public organizations act
Formal Methods of Argumentation as Models of Engineering Design Decisions and Processes
Complex engineering projects comprise many individual design decisions. As these decisions are made over the course of months, even years, and across different teams of engineers, it is common for them to be based on different, possibly conflicting assumptions. The longer these inconsistencies go undetected, the costlier they are to resolve. Therefore it is important to spot them as early as possible. There is currently no software aimed explicitly at detecting inconsistencies in interrelated design decisions. This thesis is a step towards the development of such tools. We use formal methods of argumentation, a branch of artificial intelligence, as the foundation of a logical model of design decisions capable of handling inconsistency. It has three parts. First, argumentation is used to model the pros and cons of individual decisions and to reason about the possible worlds in which these arguments are justified. In the second part we study sequences of interrelated decisions. We identify cases where the arguments in one decision invalidate the justification for another decision, and develop a measure of the impact that choosing a specific option has on the consistency of the overall design. The final part of the thesis is concerned with non-deductive arguments, which are used in design debates, for example to draw analogies between past and current problems. Our model integrates deductive and non-deductive arguments side-by-side. This work is supported by our collaboration with the engineering department of Queen’s University Belfast and an industrial partner. The thesis contains two case studies of realistic problems and parts of it were implemented as software prototypes. We also give theoretical results demonstrating the internal consistency of our model
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