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Federated Conference on Computer science and Information Systems (FedCSIS)

作者： Francisco Herrera Dept. of Computer Science and Artificial Intelligence Andalusian Institute of Data Science and Computational Intelligence (DaSCI) University of Granada Spain

ISBN: (数字)9788396960160

ISBN: (纸本)9798350359718

The emergence of deep learning at the beginning of the last decade has driven the advancement of complex models, culminating in the development of large language models and generative AI. These models represent the summit of size and complexity. Explainability should be an option that plays a key role in enabling understandable the AI-assisted decision-making and ensuring accountability. This contribution delves into the complexities of explainable artificial intelligence (XAI) through various perspectives, considering the extensive and growing body of literature. Our discussion begins by addressing the challenges posed by complex data, models, and high-risk scenarios. Given the rapid growth of the field, it is essential to tackle the criticisms and challenges that emerge as it matures, requiring thorough exploration. This contribution explores them, along with three aspects that may shed light on them. First, it is focused on the lack of definitional cohesion, examining how and why is defined XAI from the perspectives of audience and understanding. Second, it explores XAI explanations, bridging the gap between complex AI models and human understanding. Third, it is crucial to consider how to analyze and evaluate the maturity level of explainability, from a triple dimension, practicality, governance and auditability.

关键词： Deep learning Computer science Explainable AI Generative AI Large language models computational modeling Decision making data models Complexity theory

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A Survey on Semi-Supervised Semantic Segmentation

arXiv

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arXiv 2023年

作者： Peláez-Vegas, Adrián Mesejo, Pablo Luengo, Julián The Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Granada Granada18071 Spain

Semantic segmentation is one of the most challenging tasks in computer vision. However, in many applications, a frequent obstacle is the lack of labeled images, due to the high cost of pixel-level labeling. In this scenario, it makes sense to approach the problem from a semi-supervised point of view, where both labeled and unlabeled images are exploited. In recent years this line of research has gained much interest and many approaches have been published in this direction. Therefore, the main objective of this study is to provide an overview of the current state of the art in semi-supervised semantic segmentation, offering an updated taxonomy of all existing methods to date. This is complemented by an experimentation with a variety of models representing all the categories of the taxonomy on the most widely used becnhmark datasets in the literature, and a final discussion on the results obtained, the challenges and the most promising lines of future research. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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A Large-Scale Group Decision-Making Approach Employing Large Language Models to Detect Assertive Groups 23

A Large-Scale Group Decision-Making Approach Employing Large...

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23rd International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques, SoMeT 2024

作者： Carbó-García, Santiago Trillo, José Ramón Tapia, Juan Miguel Pérez, Ignacio Javier Herrera-Viedma, Enrique Cabrerizo, Francisco Javier Andalusian Research Institute in Data Science and Computational Intelligence Department of Computer Science and Artificial Intelligence University of Granada Granada Spain Department of Quantitative Methods for Business and Economics University of Granada Granada Spain

ISBN: (纸本)9781643685380

Large-Scale Group Decision-Making, propelled by the advent of Large Language Models and the imperative for assertiveness in decision-making processes, emerges as a pivotal area of research. This paper navigates through the landscape of Large-Scale Group Decision-Making, delineating its significance across diverse domains, including social networks and e-democracy. Amidst its nascent status, Large-Scale Group Decision-Making encounters formidable challenges, particularly in information management and fostering consensus among a multitude of experts. This paper aims to illuminate the goals and hurdles facing Large-Scale Group Decision-Making approaches through an exhaustive review of contemporary literature and methodologies. By confronting these challenges head-on, Large-Scale Group Decision-Making holds the potential to redefine decision-making paradigms, bolster assertiveness, and elevate collective problem-solving capabilities in an increasingly interconnected world. © 2024 IOS Press. All rights reserved.

关键词： Information management

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A Large Scale Group Decision Making System Based on Sentiment Analysis Cluster

SSRN

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SSRN 2022年

作者： Trillo, José Ramón Herrera-Viedma, Enrique Morente-Molinera, Juan Antonio Cabrerizo, Francisco Javier Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Granada Granada18071 Spain

Nowadays, Group Decision Making is an everyday occurrence in different scenarios, such as marketing or social networks. These social networks have facilitated communication between experts because they do not need to meet in person. However, communication between experts through the Internet generates three problems: the management of large amounts of information, the fact that experts often provide their information using natural language, and the lack of analysis of experts’ intentions. In this paper, we propose a novel Large Scale Group Decision Making method to manage the information generated by a large number of experts, using a sentiment analysis with bag-of-words approach. This approach makes it possible to detect the degree of positivity and aggressiveness of each expert. Once the behaviours have been detected, the experts are grouped according to them and, for each group, a weight and a unique preference relation are obtained. Moreover, we propose an optimised consensus analysis process, where it is not necessary to compare all experts with each other, but only groups of experts. Thanks to our method, we can conduct studies of experts’ behaviour in large scale environments. © 2022, The Authors. All rights reserved.

关键词： Sentiment analysis

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Predict. Optimize. Revise. On Forecast and Policy Stability in Energy Management Systems

arXiv

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arXiv 2024年

作者： Genov, Evgenii Ruddick, Julian Bergmeir, Christoph Vafaeipour, Majid Coosemans, Thierry García, Salvador Messagie, Maarten Belgium Data Science and Computational Intelligence Andalusian Institute DaSCI Andalucía Spain Department of Computer Science and Artificial Intelligence University of Granada Andalucía Granada Spain

This research addresses the challenge of integrating forecasting and optimization in energy management systems, focusing on the impacts of switching costs, forecast accuracy, and stability. It proposes a novel framework for analyzing online optimization problems with switching costs and enabled by deterministic and probabilistic forecasts. Through empirical evaluation and theoretical analysis, the research reveals the balance between forecast accuracy, stability, and switching costs in shaping policy performance. Conducted in the context of battery scheduling within energy management applications, it introduces a metric for evaluating probabilistic forecast stability and examines the effects of forecast accuracy and stability on optimization outcomes using the real-world case of the Citylearn 2022 competition. Findings indicate that switching costs significantly influence the trade-off between forecast accuracy and stability, highlighting the importance of integrated systems that enable collaboration between forecasting and operational units for improved decision-making. The study shows that committing to a policy for longer periods can be advantageous over frequent updates. Results also show a correlation between forecast stability and policy performance, suggesting that stable forecasts can mitigate switching costs. The proposed framework provides valuable insights for energy sector decision-makers and forecast practitioners when designing the operation of an energy management system. © 2024, CC BY.

关键词： Forecasting

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Reviving stagnated debates in Group Decision Making environments with high number of alternatives 10

Reviving stagnated debates in Group Decision Making environm...

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10th International Conference on Information Technology and Quantitative Management, ITQM 2023

作者： Morente-Molinera, J.A. Barragán-Guzmán, M. Trillo, J.R. Martínez-Sánchez, M.A. Cabrerizo, F.J. Herrera-Viedma, E. Andalusian Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain Department of Social Work and Social Services University of Granada Granada18001 Spain

In group decision-making, experts try to obtain a consensus to determine how to order a series of alternatives. A consensus is a decision that reflects the opinions of every group member. Consensus requires discussion and deliberation between the group members. During the process, it is normal that the discussion process to stagnate. In this article, we present a new method of group decision-making that solves the stagnation of the process by including new information. The timing of this is determined by a process stagnation analysis. Fuzzy Ontologies allow experts to work in environments with large numbers of alternatives. The method takes into account the experts' rankings of alternatives to determine which criteria the experts seem to like the most. When introducing new information into the debate, experts are given the possibility to explore alternatives that are very different from those already seen or to look for alternatives that meet the criteria that are preferred the most. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the Tenth International Conference on Information Technology and Quantitative Management.

关键词： Ontology

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On Children's Exploration, Aha! Moments and Explanations in Model Building for Self-Regulated Problem-Solving

On Children's Exploration, Aha! Moments and Explanations in ...

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2022 Workshop on AI Evaluation Beyond Metrics, EBeM 2022

作者： Charisi, Vicky Díaz-Rodríguez, Natalia Mawhin, Barbara Merino, Luis Joint Research Centre European Commission Seville Spain DaSCI Andalusian Institute in Data Science and Computational Intelligence Univers. of Granada Spain Human Factors Department EBT-Salient Aero Foundation Spain Service Robotics Laboratory University Pablo de Olavide Seville Spain

In certain problem-solving tasks that require Human-AI interactions, a mutual understanding of the reasoning behind the performed actions can benefit both humans and artificial agents. However, identifying and predicting the cognitive strategies involved in such a hybrid setting, especially in novel, self-regulated exploratory tasks, is a challenging endeavour. Our aim is to identify behavioural properties relevant to young children's cognitive strategies that are present in problem-solving, with an emphasis on the Aha! moment as an intermediate step between exploratory actions, that typically relate to the development of tacit knowledge, and the generation of explanations that requires explicit knowledge. We use data from existing, previously published, behavioural studies with children 5 to 7 years old to explore these mechanisms in two self-regulated problem-solving tasks. In addition, we reflect on our observations of an Artificial Agent (Q-learning algorithm) that learns to solve the same task. Our findings indicate that while in current reinforcement learning practice, detecting the moment of the cognitive transformation of the problem representation normally translates into observing convergence curves of the objective functions being optimized, in young children this involves more complex behavioural properties, such as verbal metacognition. These behavioural processes can be used as a proxy for the identification of the Aha! moment. Finally, we propose a conceptual map which integrates the observed behaviours that are used to detect, communicate and corroborate learning both in humans and machines and we discuss the association of children's exploratory behaviours, the Aha! moments and ultimately their explanation generation. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Curve fitting

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A Group Decision-Making Approach Leveraging Preference Relations Derived from Large Language Model 11

A Group Decision-Making Approach Leveraging Preference Relat...

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11th International Conference on Information Technology and Quantitative Management, ITQM 2024

作者： Trillo, José Ramón Martínez, María Ángeles Zadrozny, Slawomir Kacprzyk, Janusz Herrera-Viedma, Enrique Cabrerizo, Francisco Javier Department of Computer Science and Artificial Intelligence University of Granada Granada18071 Spain Department of Social Work and Social Services University of Granada Granada18001 Spain Systems Research Institute Polish Academy of Sciences Newelska 6 Warsaw01-447 Poland Andalusian Research Institute of Data Science and Computational Intelligence DaSCI University of Granada Granada18071 Spain

Group decision-making involves selecting among limited options. Experts share their perspectives in a comparative debate but then evaluate alternatives using preference relations, which can result in inconsistencies between their expressions and assessments. To address this, a method is proposed that automates the generation of these relationships from the debate comments, classifying them into positive and negative using sentiment analysis, namely with the Large Language Model. A new operator is introduced that weights these comments to calculate preference relations. Furthermore, modification of the relationships is allowed if the experts so wish. Moreover, another operator is incorporated that adjusts the weight of each expert according to his or her active participation in the discussion, assigning more weight to those who contribute more comments. Finally, this innovative method promotes coherence and equal participation in group decision-making by employing an innovative sentiment analysis detection system. © 2024 The Authors.

关键词： Modeling languages

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An analysis of local and global solutions to address big data imbalanced classification: A case study with smote preprocessing 7th

An analysis of local and global solutions to address big dat...

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7th International Conference on Cloud Computing and Big data, JCC and BD 2019

作者： Basgall, María José Hasperué, Waldo Naiouf, Marcelo Fernández, Alberto Herrera, Francisco UNLP CONICET III-LIDI La Plata Argentina CIC-PBA Facultad de Informática-Universidad Nacional de La Plata La Plata Argentina University of Granada Granada Spain DaSCI Andalusian Institute of Data Science and Computational Intelligence University of Granada Granada Spain

ISBN: (纸本)9783030277123

Addressing the huge amount of data continuously generated is an important challenge in the Machine Learning field. The need to adapt the traditional techniques or create new ones is evident. To do so, distributed technologies have to be used to deal with the significant scalability constraints due to the Big data context. In many Big data applications for classification, there are some classes that are highly underrepresented, leading to what is known as the imbalanced classification problem. In this scenario, learning algorithms are often biased towards the majority classes, treating minority ones as out-liers or noise. Consequently, preprocessing techniques to balance the class distribution were developed. This can be achieved by suppressing majority instances (undersampling) or by creating minority examples (oversam-pling). Regarding the oversampling methods, one of the most widespread is the SMOTE algorithm, which creates artificial examples according to the neighborhood of each minority class instance. In this work, our objective is to analyze the SMOTE behavior in Big data as a function of some key aspects such as the oversampling degree, the neighborhood value and, specially, the type of distributed design (local vs. global). © Springer Nature Switzerland AG 2019.

关键词： Scalability

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Large-scale group decision-making: A bibliometric study

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Procedia Computer science 2024年 242卷 1198-1205页

作者： Juan Carlos González-Quesada José Ramón Trillo María Ángeles Martínez Enrique Herrera-Viedma Francisco Javier Cabrerizo Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) University of Granada 18071 Granada Spain Department of Social Work and Social Services University of Granada 18001 Granada Spain

The process of large-scale group decision-making pertains to the facilitation of decisions within expansive assemblies of individuals across a range of settings, spanning from corporate entities to local communities and governmental bodies. This approach is indispensable given the intricate and heterogeneous nature of the opinions, interests, and perspectives that are prevalent in such extensive collectives. In the realm of large-scale group decision-making, a diverse array of methods and strategies come into play. These include proficient facilitation, leveraging technology, employing various voting methods, and engaging in structured deliberation. These techniques collectively serve to streamline the decision-making process and yield equitable and satisfactory results for all involved parties. Given the fact that this topic has become a hot topic in recent years, the objective of this work is to conduct a bibliometric study on large-scale group decision-making to provide an overview of this emerging area within group decision-making. Aspects such as the main contributing journals, relevant authors and affiliated countries, as well as the most cited documents, authors, and keywords, are examined. This analysis will offer a deeper understanding of the current state and trends in large-scale group decision-making, emphasizing its significance and applicability across various fields of study and practice.

关键词： Large-scale group decision Fuzzy decision-making Bibliometrics

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