Loading...
Research Project
Argumentation Dialogues in Web-based GDSS: an approach using Machine Learning Techniques
Funder
Authors
Publications
Supporting Argumentation Dialogues in Group Decision Support Systems: An Approach Based on Dynamic Clustering
Publication . Conceição, Luís; Rodrigues, Vasco; Meira, Jorge; Marreiros, Goreti; Novais, Paulo
Group decision support systems (GDSSs) have been widely studied over the recent decades. The Web-based group decision support systems appeared to support the group decision-making process by creating the conditions for it to be effective, allowing the management and participation in the process to be carried out from any place and at any time. 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) intends to classify opinions at the aspect level and identify the elements of an opinion. Intelligent reports for GDSS provide decision makers with accurate information about each decision-making round. Applying ABSA techniques to group decision making context results in the automatic identification of alternatives and criteria, for instance. This automatic identification is essential to reduce the time decision makers take to step themselves up on group decision support systems and to offer them various insights and knowledge on the discussion they are participating in. In this work, we propose and implement a methodology that uses an unsupervised technique and clustering to group arguments on topics around a specific alternative, for example, or a discussion comparing two alternatives. We experimented with several combinations of word embedding, dimensionality reduction techniques, and different clustering algorithms to achieve the best approach. The best method consisted of applying the KMeans++ clustering technique, using SBERT as a word embedder with UMAP dimensionality reduction. These experiments achieved a silhouette score of 0.63 with eight clusters on the baseball dataset, which wielded good cluster results based on their manual review and word clouds. We obtained a silhouette score of 0.59 with 16 clusters on the car brand dataset, which we used as an approach validation dataset. With the results of this work, intelligent reports for GDSS become even more helpful, since they can dynamically organize the conversations taking place by grouping them on the arguments used.
A Multiple Criteria Decision Analysis Framework for Dispersed Group Decision-Making Contexts
Publication . Carneiro, João; Martinho, Diogo; Alves, Patrícia; Conceição, Luís; Marreiros, Goreti; Novais, Paulo
To support Group Decision-Making processes when participants are dispersed is a complex task. The biggest challenges are related to communication limitations that impede decision-makers to take advantage of the benefits associated with face-to-face Group Decision-Making processes. Several approaches that intend to aid dispersed groups attaining decisions have been applied to Group Decision Support Systems. However, strategies to support decision-makers in reasoning, understanding the reasons behind the different recommendations, and promoting the decision quality are very limited. In this work, we propose a Multiple Criteria Decision Analysis Framework that intends to overcome those limitations through a set of functionalities that can be used to support decision-makers attaining more informed, consistent, and satisfactory decisions. These functionalities are exposed through a microservice, which is part of a Consensus-Based Group Decision Support System and is used by autonomous software agents to support decision-makers according to their specific needs/interests. We concluded that the proposed framework greatly facilitates the definition of important procedures, allowing decision-makers to take advantage of deciding as a group and to understand the reasons behind the different recommendations and proposals.
Organizational Units
Description
Keywords
Contributors
Funders
Funding agency
Fundação para a Ciência e a Tecnologia
Funding programme
Funding Award Number
SFRH/BD/137150/2018