2024
Silva, A. C.; Machado, J.; Sampaio, P.
Predictive quality model for customer defects Journal Article
Em: TQM Journal, vol. 36, não 9, pp. 155-174, 2024, ISSN: 17542731, (cited By 0).
Resumo | Links | BibTeX | Etiquetas: Artificial intelligence techniques; Customer complaints; Data accessibility; Decisions makings; Digital transformation; Digitisation; Machine learning and customer complaint; Machine-learning; Predictive models; Quality 4.0, Data mining; Forecasting; Industry 4.0; Learning systems; Machine learning; Metadata; Quality control; Quality management; Sales, Decision making
@article{Silva2024155,
title = {Predictive quality model for customer defects},
author = {A. C. Silva and J. Machado and P. Sampaio},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195506444&doi=10.1108%2fTQM-09-2023-0302&partnerID=40&md5=568e7612936588c8b44daaa72b93058c},
doi = {10.1108/TQM-09-2023-0302},
issn = {17542731},
year = {2024},
date = {2024-01-01},
journal = {TQM Journal},
volume = {36},
number = {9},
pages = {155-174},
publisher = {Emerald Publishing},
abstract = {Purpose: In the context of the journey toward digital transformation and the realization of a fully connected factory, concepts such as data science, artificial intelligence (AI), machine learning (ML) and even predictive models emerge as indispensable pillars. Given the relevance of these topics, the present study focused on the analysis of customer complaint data, employing ML techniques to anticipate complaint accountability. The primary objective was to enhance data accessibility, harnessing the potential of ML models to optimize the complaint handling process and thereby positively contribute to data-driven decision-making. This approach aimed not only to reduce the number of units to be analyzed and customer response time but also to underscore the pressing need for a paradigm shift in quality management. The application of AI techniques sought to enhance not only the efficiency of the complaint handling process and data accessibility but also to demonstrate how the integration of these innovative approaches could profoundly transform the way quality is conceived and managed within organizations. Design/methodology/approach: To conduct this study, real customer complaint data from an automotive company was utilized. Our main objective was to highlight the importance of artificial intelligence (AI) techniques in the context of quality. To achieve this, we adopted a methodology consisting of 10 distinct phases: business analysis and understanding; project plan definition; sample definition; data exploration; data processing and pre-processing; feature selection; acquisition of predictive models; evaluation of the models; presentation of the results; and implementation. This methodology was adapted from data mining methodologies referenced in the literature, taking into account the specific reality of the company under study. This ensured that the obtained results were applicable and replicable across different fields, thereby strengthening the relevance and generalizability of our research findings. Findings: The achieved results not only demonstrated the ability of ML models to predict complaint accountability with an accuracy of 64%, but also underscored the significance of the adopted approach within the context of Quality 4.0 (Q4.0). This study served as a proof of concept in complaint analysis, enabling process automation and the development of a guide applicable across various areas of the company. The successful integration of AI techniques and Q4.0 principles highlighted the pressing need to apply concepts of digitization and artificial intelligence in quality management. Furthermore, it emphasized the critical importance of data, its organization, analysis and availability in driving digital transformation and enhancing operational efficiency across all company domains. In summary, this work not only showcased the advancements achieved through ML application but also emphasized the pivotal role of data and digitization in the ongoing evolution of Quality 4.0. Originality/value: This study presents a significant contribution by exploring complaint data within the organization, an area lacking investigation in real-world contexts, particularly focusing on practical applications. The development of standardized processes for data handling and the application of predictions for classification models not only demonstrated the viability of this approach but also provided a valuable proof of concept for the company. Most importantly, this work was designed to be replicable in other areas of the factory, serving as a fundamental basis for the company’s data scientists. Until then, limited data access and lack of automation in its treatment and analysis represented significant challenges. In the context of Quality 4.0, this study highlights not only the immediate advantages for decision-making and predicting complaint outcomes but also the long-term benefits, including clearer and standardized processes, data-driven decision-making and improved analysis time. Thus, this study not only underscores the importance of data and the application of AI techniques in the era of quality but also fills a knowledge gap by providing an innovative and replicable approach to complaint analysis within the organization. In terms of originality, this article stands out for addressing an underexplored area and providing a tangible and applicable solution for the company, highlighting the intrinsic value of aligning quality with AI and digitization. © 2024, Anabela Costa Silva, José Machado and Paulo Sampaio.},
note = {cited By 0},
keywords = {Artificial intelligence techniques; Customer complaints; Data accessibility; Decisions makings; Digital transformation; Digitisation; Machine learning and customer complaint; Machine-learning; Predictive models; Quality 4.0, Data mining; Forecasting; Industry 4.0; Learning systems; Machine learning; Metadata; Quality control; Quality management; Sales, Decision making},
pubstate = {published},
tppubtype = {article}
}
Purpose: In the context of the journey toward digital transformation and the realization of a fully connected factory, concepts such as data science, artificial intelligence (AI), machine learning (ML) and even predictive models emerge as indispensable pillars. Given the relevance of these topics, the present study focused on the analysis of customer complaint data, employing ML techniques to anticipate complaint accountability. The primary objective was to enhance data accessibility, harnessing the potential of ML models to optimize the complaint handling process and thereby positively contribute to data-driven decision-making. This approach aimed not only to reduce the number of units to be analyzed and customer response time but also to underscore the pressing need for a paradigm shift in quality management. The application of AI techniques sought to enhance not only the efficiency of the complaint handling process and data accessibility but also to demonstrate how the integration of these innovative approaches could profoundly transform the way quality is conceived and managed within organizations. Design/methodology/approach: To conduct this study, real customer complaint data from an automotive company was utilized. Our main objective was to highlight the importance of artificial intelligence (AI) techniques in the context of quality. To achieve this, we adopted a methodology consisting of 10 distinct phases: business analysis and understanding; project plan definition; sample definition; data exploration; data processing and pre-processing; feature selection; acquisition of predictive models; evaluation of the models; presentation of the results; and implementation. This methodology was adapted from data mining methodologies referenced in the literature, taking into account the specific reality of the company under study. This ensured that the obtained results were applicable and replicable across different fields, thereby strengthening the relevance and generalizability of our research findings. Findings: The achieved results not only demonstrated the ability of ML models to predict complaint accountability with an accuracy of 64%, but also underscored the significance of the adopted approach within the context of Quality 4.0 (Q4.0). This study served as a proof of concept in complaint analysis, enabling process automation and the development of a guide applicable across various areas of the company. The successful integration of AI techniques and Q4.0 principles highlighted the pressing need to apply concepts of digitization and artificial intelligence in quality management. Furthermore, it emphasized the critical importance of data, its organization, analysis and availability in driving digital transformation and enhancing operational efficiency across all company domains. In summary, this work not only showcased the advancements achieved through ML application but also emphasized the pivotal role of data and digitization in the ongoing evolution of Quality 4.0. Originality/value: This study presents a significant contribution by exploring complaint data within the organization, an area lacking investigation in real-world contexts, particularly focusing on practical applications. The development of standardized processes for data handling and the application of predictions for classification models not only demonstrated the viability of this approach but also provided a valuable proof of concept for the company. Most importantly, this work was designed to be replicable in other areas of the factory, serving as a fundamental basis for the company’s data scientists. Until then, limited data access and lack of automation in its treatment and analysis represented significant challenges. In the context of Quality 4.0, this study highlights not only the immediate advantages for decision-making and predicting complaint outcomes but also the long-term benefits, including clearer and standardized processes, data-driven decision-making and improved analysis time. Thus, this study not only underscores the importance of data and the application of AI techniques in the era of quality but also fills a knowledge gap by providing an innovative and replicable approach to complaint analysis within the organization. In terms of originality, this article stands out for addressing an underexplored area and providing a tangible and applicable solution for the company, highlighting the intrinsic value of aligning quality with AI and digitization. © 2024, Anabela Costa Silva, José Machado and Paulo Sampaio.
2020
Oliveira, S.; Esteves, M.; Cernadas, R.; Abelha, A.; Machado, J.
The development of a business intelligence web application to support the decision-making process regarding absenteeism in the workplace Proceedings Article
Em: A., Montenegro Marin C. E. Ferras C. Rocha (Ed.): pp. 104-113, Springer, 2020, ISSN: 21945357, (cited By 1; Conference of International Conference on Information Technology and Systems, ICITS 2020 ; Conference Date: 5 February 2020 Through 7 February 2020; Conference Code:236869).
Resumo | Links | BibTeX | Etiquetas: Absenteeism; Decision making process; Design and Development; Information and Communication Technologies; Quality healthcare; Reduce time; WEB application; Work accidents, Competitive intelligence; Information analysis; Personnel; Telemedicine, Decision making
@inproceedings{Oliveira2020104,
title = {The development of a business intelligence web application to support the decision-making process regarding absenteeism in the workplace},
author = {S. Oliveira and M. Esteves and R. Cernadas and A. Abelha and J. Machado},
editor = {Montenegro Marin C. E. Ferras C. Rocha A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85080919971&doi=10.1007%2f978-3-030-40690-5_11&partnerID=40&md5=ed9eefa756abc7615c2439c43b93b034},
doi = {10.1007/978-3-030-40690-5_11},
issn = {21945357},
year = {2020},
date = {2020-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {1137 AISC},
pages = {104-113},
publisher = {Springer},
abstract = {Nowadays, one of the biggest concerns of industries all over the world is situations regarding absenteeism, since it has a great impact on the productivity and economy of companies, as well as on the health of their employees. The major causes of absenteeism appear to be work accidents and sickness leaves, which lead to the attempt by companies of understanding how the workload is related to the health of their collaborators and, consequently, to absenteeism. Thus, this paper proposes the design and development of a Web Application based on Business Intelligence indicators in order to help the health and human resources professionals of a Portuguese company analyse the relation between absenteeism and the health and lifestyle of employees, with the intention of concluding whether the work executed on the company is harming workers’ health. Furthermore, it is intended to discover the principal motives for the numerous and more frequent absences in this company, so that it is possible to decrease the absenteeism rate and, hence, improve the decision-making process. This platform will also provide higher quality healthcare and the possibility to find patterns in the absence of collaborators, as well as reduce time-waste and errors. © Springer Nature Switzerland AG 2020.},
note = {cited By 1; Conference of International Conference on Information Technology and Systems, ICITS 2020 ; Conference Date: 5 February 2020 Through 7 February 2020; Conference Code:236869},
keywords = {Absenteeism; Decision making process; Design and Development; Information and Communication Technologies; Quality healthcare; Reduce time; WEB application; Work accidents, Competitive intelligence; Information analysis; Personnel; Telemedicine, Decision making},
pubstate = {published},
tppubtype = {inproceedings}
}
Nowadays, one of the biggest concerns of industries all over the world is situations regarding absenteeism, since it has a great impact on the productivity and economy of companies, as well as on the health of their employees. The major causes of absenteeism appear to be work accidents and sickness leaves, which lead to the attempt by companies of understanding how the workload is related to the health of their collaborators and, consequently, to absenteeism. Thus, this paper proposes the design and development of a Web Application based on Business Intelligence indicators in order to help the health and human resources professionals of a Portuguese company analyse the relation between absenteeism and the health and lifestyle of employees, with the intention of concluding whether the work executed on the company is harming workers’ health. Furthermore, it is intended to discover the principal motives for the numerous and more frequent absences in this company, so that it is possible to decrease the absenteeism rate and, hence, improve the decision-making process. This platform will also provide higher quality healthcare and the possibility to find patterns in the absence of collaborators, as well as reduce time-waste and errors. © Springer Nature Switzerland AG 2020.
2014
Silva, E.; Alpuim, A.; Cardoso, L.; Marins, F.; Quintas, C.; Portela, C. F.; Santos, M. F.; Machado, J.; Abelha, A.
Business intelligence and nosocomial infection decision making Book Chapter
Em: pp. 193-215, IGI Global, 2014, ISBN: 9781466664784; 1466664770; 9781466664777, (cited By 3).
Resumo | Links | BibTeX | Etiquetas: Artificial intelligence; Decision support systems; Health care; Information analysis; Information management, Clinical decision support systems; Data manipulations; Data mining models; Decision making process; Health care professionals; Healthcare environments; Healthcare organizations; Nosocomial infection, Decision making
@inbook{Silva2014193,
title = {Business intelligence and nosocomial infection decision making},
author = {E. Silva and A. Alpuim and L. Cardoso and F. Marins and C. Quintas and C. F. Portela and M. F. Santos and J. Machado and A. Abelha},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84946131048&doi=10.4018%2f978-1-4666-6477-7.ch010&partnerID=40&md5=6b84b26387401612ee03872a886f1404},
doi = {10.4018/978-1-4666-6477-7.ch010},
isbn = {9781466664784; 1466664770; 9781466664777},
year = {2014},
date = {2014-01-01},
journal = {Integration of Data Mining in Business Intelligence Systems},
pages = {193-215},
publisher = {IGI Global},
abstract = {The implementation of Business Intelligence tools in healthcare organizations helps the managers and the healthcare professionals in their decision making process through data manipulation and data analysis. The main goal of this chapter is to evaluate the applicability of the Business Intelligence tools and concepts to healthcare and their performance as a Clinical Decision Support System, analyzing the evolution of nosocomial infection in the Centro Hospitalar do Porto, by defining a set of indicators that can help the nosocomial infection management and inducing Data Mining models to predict the occurrence of nosocomial infections (sensitivity of 91%). The knowledge obtained with the analysis of the indicators and the knowledge obtained with the nosocomial infection prediction can be applied by healthcare professionals in their decision making. Through the analysis of the data collected, Business Intelligence tools help overcome the problems associated with the complexity, heterogeneity, and distributiveness present in the healthcare environment. © 2015, IGI Global.},
note = {cited By 3},
keywords = {Artificial intelligence; Decision support systems; Health care; Information analysis; Information management, Clinical decision support systems; Data manipulations; Data mining models; Decision making process; Health care professionals; Healthcare environments; Healthcare organizations; Nosocomial infection, Decision making},
pubstate = {published},
tppubtype = {inbook}
}
The implementation of Business Intelligence tools in healthcare organizations helps the managers and the healthcare professionals in their decision making process through data manipulation and data analysis. The main goal of this chapter is to evaluate the applicability of the Business Intelligence tools and concepts to healthcare and their performance as a Clinical Decision Support System, analyzing the evolution of nosocomial infection in the Centro Hospitalar do Porto, by defining a set of indicators that can help the nosocomial infection management and inducing Data Mining models to predict the occurrence of nosocomial infections (sensitivity of 91%). The knowledge obtained with the analysis of the indicators and the knowledge obtained with the nosocomial infection prediction can be applied by healthcare professionals in their decision making. Through the analysis of the data collected, Business Intelligence tools help overcome the problems associated with the complexity, heterogeneity, and distributiveness present in the healthcare environment. © 2015, IGI Global.
2009
Costa, R.; Novais, P.; Lima, L.; Carneiro, D.; Samico, D.; Oliveira, J.; Machado, J.; Neves, J.
VirtualECare: Intelligent assisted living Proceedings Article
Em: pp. 138-144, London, 2009, ISSN: 18678211, (cited By 18; Conference of 1st International Conference on Electronic Healthcare, eHealth 2008 ; Conference Date: 8 September 2008 Through 9 September 2008; Conference Code:85965).
Resumo | Links | BibTeX | Etiquetas: Active ageing; Ambient intelligence; Assisted living; Ehealth; First year; Idea generation; Proactiveness, Decision making, Health care
@inproceedings{Costa2009138,
title = {VirtualECare: Intelligent assisted living},
author = {R. Costa and P. Novais and L. Lima and D. Carneiro and D. Samico and J. Oliveira and J. Machado and J. Neves},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84885886293&doi=10.1007%2f978-3-642-00413-1_17&partnerID=40&md5=c95a485703b42027cd0737104d7255e0},
doi = {10.1007/978-3-642-00413-1_17},
issn = {18678211},
year = {2009},
date = {2009-01-01},
journal = {Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering},
volume = {1 LNICST},
pages = {138-144},
address = {London},
abstract = {Innovative healthcare projects are arising in today's society, normally presenting as major advantage the reduction of care provider's costs. Being these advantage a legitimate one, we are trying to take it a step forward through the use of proactiveness, decision making techniques, idea generation, argumentation and quality, not only of the in transit information, but also of the provided service as well. With these objectives in mind, the VirtualECare project was born. In this paper we are going to briefly present the project and make a position of the actual developments in this first year of work. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2009.},
note = {cited By 18; Conference of 1st International Conference on Electronic Healthcare, eHealth 2008 ; Conference Date: 8 September 2008 Through 9 September 2008; Conference Code:85965},
keywords = {Active ageing; Ambient intelligence; Assisted living; Ehealth; First year; Idea generation; Proactiveness, Decision making, Health care},
pubstate = {published},
tppubtype = {inproceedings}
}
Innovative healthcare projects are arising in today's society, normally presenting as major advantage the reduction of care provider's costs. Being these advantage a legitimate one, we are trying to take it a step forward through the use of proactiveness, decision making techniques, idea generation, argumentation and quality, not only of the in transit information, but also of the provided service as well. With these objectives in mind, the VirtualECare project was born. In this paper we are going to briefly present the project and make a position of the actual developments in this first year of work. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2009.
Santos, M. F.; Portela, F.; Vilas-Boas, M.; Machado, J.; Abelha, A.; Neves, J.; Silva, A.; Rua, F.
Information architecture for intelligent decision support in intensive medicine Journal Article
Em: WSEAS Transactions on Computers, vol. 8, não 5, pp. 810-819, 2009, ISSN: 11092750, (cited By 18).
Resumo | Links | BibTeX | Etiquetas: Architectural design; Artificial intelligence; Database systems; Decision support systems; Decision theory; Information retrieval; Information science; Intensive care units; Knowledge management; Mergers and acquisitions; Real time systems, Decision making, Information models; INTCare; Intelligent decision support systems; Intensive care; Knowledge discovery in databases; Real-time data acquisition
@article{Santos2009810,
title = {Information architecture for intelligent decision support in intensive medicine},
author = {M. F. Santos and F. Portela and M. Vilas-Boas and J. Machado and A. Abelha and J. Neves and A. Silva and F. Rua},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-69549098007&partnerID=40&md5=eadb2fee9457fd205183fd9b3262ce5b},
issn = {11092750},
year = {2009},
date = {2009-01-01},
journal = {WSEAS Transactions on Computers},
volume = {8},
number = {5},
pages = {810-819},
abstract = {Daily, a great amount of data that is gathered in intensive care units, which makes intensive medicine a very attractive field for applying knowledge discovery in databases. Previously unknown knowledge can be extracted from that data in order to create prediction and decision models. The challenge is to perform those tasks in real-time, in order to assist the doctors in the decision making process. Furthermore, the models should be continuously assessed and optimized, if necessary, to maintain a certain accuracy. In this paper we propose an information architecture to support an adjustment to the INTCare system, an intelligent decision support system for intensive medicine. We focus on the automatization of data acquisition avoiding human intervention, describing its steps and some requirements.},
note = {cited By 18},
keywords = {Architectural design; Artificial intelligence; Database systems; Decision support systems; Decision theory; Information retrieval; Information science; Intensive care units; Knowledge management; Mergers and acquisitions; Real time systems, Decision making, Information models; INTCare; Intelligent decision support systems; Intensive care; Knowledge discovery in databases; Real-time data acquisition},
pubstate = {published},
tppubtype = {article}
}
Daily, a great amount of data that is gathered in intensive care units, which makes intensive medicine a very attractive field for applying knowledge discovery in databases. Previously unknown knowledge can be extracted from that data in order to create prediction and decision models. The challenge is to perform those tasks in real-time, in order to assist the doctors in the decision making process. Furthermore, the models should be continuously assessed and optimized, if necessary, to maintain a certain accuracy. In this paper we propose an information architecture to support an adjustment to the INTCare system, an intelligent decision support system for intensive medicine. We focus on the automatization of data acquisition avoiding human intervention, describing its steps and some requirements.
2008
Carneiro, D.; Costa, R.; Novais, P.; Neves, J.; Machado, J.; Neves, J.
Simulating and monitoring ambient assisted living Proceedings Article
Em: pp. 175-182, EUROSIS, Le Havre, 2008, (cited By 23; Conference of 22nd Annual European Simulation and Modelling Conference, ESM 2008 ; Conference Date: 27 October 2008 Through 29 October 2008; Conference Code:104367).
Resumo | Links | BibTeX | Etiquetas: Agent-based architecture; Ambient assisted living; Ambient intelligence; Ehealth; Group Decision Making; Healthcare projects; Quality of information; Remote monitoring, Decision making, Modal analysis
@inproceedings{Carneiro2008175,
title = {Simulating and monitoring ambient assisted living},
author = {D. Carneiro and R. Costa and P. Novais and J. Neves and J. Machado and J. Neves},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84898929059&partnerID=40&md5=96556202044eb174de5b77651e55cff9},
year = {2008},
date = {2008-01-01},
journal = {ESM 2008 - 2008 European Simulation and Modelling Conference: Modelling and Simulation 2008},
pages = {175-182},
publisher = {EUROSIS},
address = {Le Havre},
abstract = {Researchers are giving special attention to innovative healthcare projects in order to reduce medical service costs and to deal with the population ageing. The virtual e-care was born taking those goals in mind. An intelligent and proactive system has been prototyped, supporting group decision making techniques, idea generation, argumentation and the quantification of the quality of information. In this paper it is simulated a virtual assisted living environment, based in a solid agent-based architecture. Special attention is given to the monitoring system. © 2008 EUROSIS-ETI.},
note = {cited By 23; Conference of 22nd Annual European Simulation and Modelling Conference, ESM 2008 ; Conference Date: 27 October 2008 Through 29 October 2008; Conference Code:104367},
keywords = {Agent-based architecture; Ambient assisted living; Ambient intelligence; Ehealth; Group Decision Making; Healthcare projects; Quality of information; Remote monitoring, Decision making, Modal analysis},
pubstate = {published},
tppubtype = {inproceedings}
}
Researchers are giving special attention to innovative healthcare projects in order to reduce medical service costs and to deal with the population ageing. The virtual e-care was born taking those goals in mind. An intelligent and proactive system has been prototyped, supporting group decision making techniques, idea generation, argumentation and the quantification of the quality of information. In this paper it is simulated a virtual assisted living environment, based in a solid agent-based architecture. Special attention is given to the monitoring system. © 2008 EUROSIS-ETI.
2007
Marreiros, G.; Novais, P.; MacHado, J.; Ramos, C.; Neves, J.
Modelling group decision simulation through argumentation Proceedings Article
Em: pp. 394-401, EUROSIS, St. Julians, 2007, (cited By 0; Conference of 21st Annual European Simulation and Modelling Conference, ESM 2007 ; Conference Date: 22 October 2007 Through 24 October 2007; Conference Code:104354).
Resumo | Links | BibTeX | Etiquetas: Argumentation; Decision making process; Emotional intelligence; Group decision; Group Decision Making; Group members, Decision making, Modal analysis
@inproceedings{Marreiros2007394,
title = {Modelling group decision simulation through argumentation},
author = {G. Marreiros and P. Novais and J. MacHado and C. Ramos and J. Neves},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84898430569&partnerID=40&md5=b4c3fe69cde9c209961008037eb0eb9e},
year = {2007},
date = {2007-01-01},
journal = {ESM 2007 - 2007 European Simulation and Modelling Conference: Modelling and Simulation 2007},
pages = {394-401},
publisher = {EUROSIS},
address = {St. Julians},
abstract = {Group decision making plays an important role in today's organisations. The impact of decision making is so high and complex, that rarely the decision making process is made individually. In Group Decision Argumentation, there is a set of participants, with different profiles and expertise levels, that exchange ideas or engage in a process of argumentation and counter- argumentation, negotiate, cooperate, collaborate or even discuss techniques and/or methodologies for problem solving. In this paper, it is proposed a Multi-Agent simulator for the behaviour representation of group members in a decision making process. Agents behave depending on rational and emotional intelligence and use persuasive argumentation to convince and make alternative choices.},
note = {cited By 0; Conference of 21st Annual European Simulation and Modelling Conference, ESM 2007 ; Conference Date: 22 October 2007 Through 24 October 2007; Conference Code:104354},
keywords = {Argumentation; Decision making process; Emotional intelligence; Group decision; Group Decision Making; Group members, Decision making, Modal analysis},
pubstate = {published},
tppubtype = {inproceedings}
}
Group decision making plays an important role in today's organisations. The impact of decision making is so high and complex, that rarely the decision making process is made individually. In Group Decision Argumentation, there is a set of participants, with different profiles and expertise levels, that exchange ideas or engage in a process of argumentation and counter- argumentation, negotiate, cooperate, collaborate or even discuss techniques and/or methodologies for problem solving. In this paper, it is proposed a Multi-Agent simulator for the behaviour representation of group members in a decision making process. Agents behave depending on rational and emotional intelligence and use persuasive argumentation to convince and make alternative choices.
Marreiros, G.; Santos, R.; Novais, P.; Machado, J.; Ramos, C.; Neves, J.; Bula-Cruz, J.
Argumentation-based decision making in ambient intelligence environments Proceedings Article
Em: pp. 309-322, Springer Verlag, Guimaraes, 2007, ISSN: 03029743, (cited By 9; Conference of 13th Portuguese Conference on Artificial Intelligence, EPIA 2007 Workshops ; Conference Date: 3 December 2007 Through 7 December 2007; Conference Code:71232).
Resumo | Links | BibTeX | Etiquetas: Argumentation; Artificial societies; Group decision making, Decision making, Intelligent agents; Multi agent systems; Problem solving
@inproceedings{Marreiros2007309,
title = {Argumentation-based decision making in ambient intelligence environments},
author = {G. Marreiros and R. Santos and P. Novais and J. Machado and C. Ramos and J. Neves and J. Bula-Cruz},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-38349032855&doi=10.1007%2f978-3-540-77002-2_26&partnerID=40&md5=abf9815010f8a6bea34919d2a1971707},
doi = {10.1007/978-3-540-77002-2_26},
issn = {03029743},
year = {2007},
date = {2007-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {4874 LNAI},
pages = {309-322},
publisher = {Springer Verlag},
address = {Guimaraes},
abstract = {In Group Decision Making argumentation has a crucial role; we have a set of participants, with different points of view that exchange ideas or engage in a process of argumentation and counter-argumentation, negotiate, cooperate, collaborate or even discuss techniques and/or methodologies for problem solving. In this paper we propose an argumentation-based system, where intelligent agents simulates the behaviour of individuals as members of a group in a decision making process. Our agents operate in ambience intelligent environments and behave depending on rational and emotional factors. © Springer-Verlag Berlin Heidelberg 2007.},
note = {cited By 9; Conference of 13th Portuguese Conference on Artificial Intelligence, EPIA 2007 Workshops ; Conference Date: 3 December 2007 Through 7 December 2007; Conference Code:71232},
keywords = {Argumentation; Artificial societies; Group decision making, Decision making, Intelligent agents; Multi agent systems; Problem solving},
pubstate = {published},
tppubtype = {inproceedings}
}
In Group Decision Making argumentation has a crucial role; we have a set of participants, with different points of view that exchange ideas or engage in a process of argumentation and counter-argumentation, negotiate, cooperate, collaborate or even discuss techniques and/or methodologies for problem solving. In this paper we propose an argumentation-based system, where intelligent agents simulates the behaviour of individuals as members of a group in a decision making process. Our agents operate in ambience intelligent environments and behave depending on rational and emotional factors. © Springer-Verlag Berlin Heidelberg 2007.