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Supporting and representing the group decision-making process is a complex task that requires very specific aspects. The current existing argumentation models cannot make good use of all the advantages inherent to group decision-making. There is no monitoring of the process or the possibility to provide dynamism to it. These issues can compromise t...
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... Argumentation-based dialogue models are extremely useful in contexts where a group of agents is intended to find solutions for complex decision problems using negotiation and deliberation mechanisms [1][2][3]. In addition, they allow human decision-makers to understand the reasons that led to a given decision (enhancing the acceptance of decisions) and to define mechanisms for intelligent explanations [4,5]. ...
... On the one hand, the dataset may not be sufficiently representative-for example, in comments with a level 3 rating-and, on the other hand, the fact that users are different can also have a large impact on a scale from 1 to 5, i.e., the same words have different meanings/weights for different people and people who evaluate a POI with the same rating may express it in a completely different way. As expected, the binary problem (Y = Sentiment) results were higher since the data were aggregated by the extreme ratings (1,5), where the overlapped observations were minor compared to intermediate ratings. ...
Argumentation-based dialogue models have shown to be appropriate for decision contexts in which it is intended to overcome the lack of interaction between decision-makers, either because they are dispersed, they are too many, or they are simply not even known. However, to support decision processes with argumentation-based dialogue models, it is necessary to have knowledge of certain aspects that are specific to each decision-maker, such as preferences, interests, and limitations, among others. Failure to obtain this knowledge could ruin the model’s success. In this work, we sought to facilitate the information acquisition process by studying strategies to automatically predict the tourists’ preferences (ratings) in relation to points of interest based on their reviews. We explored different Machine Learning methods to predict users’ ratings. We used Natural Language Processing strategies to predict whether a review is positive or negative and the rating assigned by users on a scale of 1 to 5. We then applied supervised methods such as Logistic Regression, Random Forest, Decision Trees, K-Nearest Neighbors, and Recurrent Neural Networks to determine whether a tourist likes/dislikes a given point of interest. We also used a distinctive approach in this field through unsupervised techniques for anomaly detection problems. The goal was to improve the supervised model in identifying only those tourists who truly like or dislike a particular point of interest, in which the main objective is not to identify everyone, but fundamentally not to fail those who are identified in those conditions. The experiments carried out showed that the developed models could predict with high accuracy whether a review is positive or negative but have some difficulty in accurately predicting the rating assigned by users. Unsupervised method Local Outlier Factor improved the results, reducing Logistic Regression false positives with an associated cost of increasing false negatives.
... Argumentation-based dialogue models are extremely useful in contexts where a group of agents is intended to find solutions for complex decision problems using negotiation and deliberation mechanisms [1][2][3]. In addition, they allow human decision-makers to understand the reasons that led to a given decision (enhancing the acceptance of decisions) and to define mechanisms for intelligent explanations [4,5]. ...
Argumentation-based dialogue models have shown to be appropriate for decision contexts in which it is intended to overcome the lack of interaction between decision-makers, either because they are dispersed, they are too many, or they are simply not even known. However, to support decision processes with argumentation-based dialogue models, it is necessary to have knowledge of certain aspects that are specific to each decision-maker, such as preferences, interests, limitations, among others. Failure to obtain this knowledge could ruin the model’s success. In this work, we intend to facilitate the acquiring information process by studying strategies to automatically predict the tourists’ preferences (ratings) in relation to points of interest based on their reviews. We explored different Machine Learning algorithms (Logistic Regression, Random Forest, Decision Tree, K-Nearest Neighbors and Recurrent Neural Networks) and Natural Language Processing strategies to predict whether a review is positive or negative and the rating assigned by users on a scale of 1 to 5. The experiments carried out showed that the developed models can predict with high accuracy whether a review is positive or negative but have some difficulty in accurately predicting the rating assigned by users.
... Each microservice implements a different business and exposes a set of resources through a RESTful API. The DialogueGames4DGDM microservice consists of a multi-agent system in which agents exchange arguments to anticipate the best solution according to the decisionmakers' preferences [14,41]. Each one of these agents represents a real decision-maker and works in favor of his/her interests. ...
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.
... Thus, we propose a system that potentiates the communication between decision-makers. Obviously, it is different to communicate in face-to-face contexts and through an online application [93,94]. So, the communication should be more structured than the one practiced in presential contexts. ...
We are living a change of paradigm regarding decision-making. On the one hand, there is a growing need to make decisions in group at both professional and personal levels, on the other hand, it is increasingly difficult for decision-makers to meet at the same place and at the same time. The Web-based Group Decision Support Systems intend to overcome this limitation, allowing decision-makers to contribute to the decision process anytime and anywhere. However, they have been defined inadequately which has been compromising its success. This work discusses the current Group Decision Support Systems limitations in terms of challenges and possible impediments for their acceptance by the organizations and propose a conceptual definition of a Web-based Group Decision Support System that intends to overcome the existing limitations and help them to affirm as a reliable and useful tool. In addition, some crucial topics are addressed, such as communication and perception, that are essential and sometimes forgotten in the support of dispersed decision-makers. We concluded that there are still some limitations, mostly in terms of models and applications, that prevent the design of higher quality systems.
... Thus, we propose a system that potentiates the communication between decision-makers. Obviously, it is different to communicate in face-to-face contexts and through an online application [11,12]. So, the communication should be more structured than the one practiced in presential contexts. ...
We are living a change of paradigm regarding decision-making. On the one hand, there is a growing need to make decisions in group at both professional and personal levels, on the other hand, it is increasingly difficult for decision-makers to meet at the same place and at the same time. The Web-based Group Decision Support Systems intend to overcome this limitation, allowing decision-makers to contribute to the decision process anytime and anywhere. However, they have been defined inadequately which has been compromising its success. In this work, we propose a conceptual definition of a Web-based Group Decision Support System that intends to overcome the existing limitations and help them to affirm as a reliable and useful tool. In addition, we address some crucial topics, such as communication and perception, that are essential and sometimes forgotten in the support of dispersed decision-makers. We concluded that there are still some limitations, mostly in terms of models and applications, that prevent the design of higher quality systems.
... So, each agent will consider the respective tourist's preferences, personality, socio-cultural aspects, mood, intentions, etc., to choose the POI to visit from the list, engaging in a real time conversation with the other agents by using argumentation. The agents argumentation will also be based on the dynamic argumentation model developed in our previous work [23,24], and will use dialogues of different types, such as negotiation and deliberation [25], to propose solutions and reach a final consensus on the list of POI to visit that better suits the group's interests and intentions. We believe this strategy can be helpful for large groups, since the agents automatic dialogues will minimize the time the tourists will need to spend in the system to reach a consensus, and will avoid the confusion inherent to chats of large groups of people, simplifying and making the choice process more organized. ...
To provide recommendations to groups of people is a complex task, especially due to the group’s heterogeneity and conflicting preferences and personalities. This heterogeneity is even deeper in occasional groups formed for predefined tour packages in tourism. Group Recommender Systems (GRS) are being designed for helping in situations like those. However, many limitations can still be found, either on their time-consuming configurations and excessive intrusiveness to build the tourists’ profile, or in their lack of concern for the tourists’ interests during the planning and tours, like feeling a greater liberty, diminish the sense of fear/being lost, increase their sense of companionship, and promote the social interaction among them without losing a personalized experience. In this paper, we propose a conceptual model that intends to enhance GRS for tourism by using gamification techniques, intelligent agents modeled with the tourists’ context and profile, such as psychological and socio-cultural aspects, and dialogue games between the agents for the post-recommendation process. Some important aspects of a GRS for tourism are also discussed, opening the way for the proposed conceptual model, which we believe will help to solve the identified limitations.
... So, each agent will consider the respective tourist's preferences, personality, socio-cultural aspects, mood, intentions, etc., to choose the POI to visit from the list, engaging in a real time conversation with the other agents by using argumentation. The agents argumentation will also be based on the dynamic argumentation model developed in our previous work [23,24], and will use dialogues of different types, such as negotiation and deliberation [25], to propose solutions and reach a final consensus on the list of POI to visit that better suits the group's interests and intentions. We believe this strategy can be helpful for large groups, since the agents automatic dialogues will minimize the time the tourists will need to spend in the system to reach a consensus, and will avoid the confusion inherent to chats of large groups of people, simplifying and making the choice process more organized. ...
To provide recommendations to groups of people is a complex task, especially due to the group's heterogeneity and conflicting preferences and personalities. This heterogeneity is even deeper in occasional groups formed for predefined tour packages in tourism. Group Recommender Systems (GRS) are being designed for helping in situations like those. However, many limitations can still be found, either on their time-consuming configurations and excessive intrusiveness to build the tourists' profile, or in their lack of concern for the tourists' interests during the planning and tours, like feeling a greater liberty, diminish the sense of fear/being lost, increase their sense of companionship, and promote the social interaction among them without losing a personalized experience. In this paper, we propose a conceptual model that intends to enhance GRS for tourism by using gamification techniques, intelligent agents modeled with the tourists' context and profile, such as psychological and socio-cultural aspects, and dialogue games between the agents for the post-recommendation process. Some important aspects of a GRS for tourism are also discussed, opening the way for the proposed conceptual model, which we believe will help to solve the identified limitations.
... They can provide information about decision-maker preferences and other simple statistical information [11,12]. The current challenge is to develop systems that can properly support the group decisionmaking process when decision-makers are dispersed [13][14][15][16]. For this, it is essential that each decision-maker can correctly define his preferences and intentions for each problem. ...
With the evolution of the organizations and technology, Group Decision Support Systems have changed to support decision-makers that cannot be together at the same place and time to make a decision. However, these systems must now be able to support the interaction between decision-makers and provide all the relevant information at the most adequate times. Failing to do so may compromise the success and the acceptance of the system. In this work it is proposed a framework for group decision using a Multiple Criteria Decision Analysis method capable of identify inconsistent assessments done by the decision-maker and identify alternatives that should be rejected by the group of decision-makers. The proposed framework allows to present more relevant information throughout the decision-making process and this way guide decision-makers in the achievement of more consensual and satisfactory decisions.
... In order to empirically evaluate the proposed framework, we implemented an argumentation-based dialogue model [10,11] designed to the group decision-making context. In order to make the scenario more complex, the agents were defined with different social aspects: behavior styles, levels of expertise and credibility [12]. ...
Dialogue games have been applied to various contexts in computer science and artificial intelligence, particularly to define interactions between autonomous software agents. However, in order to implement dialogue games, the developers need to deal with other important details besides what is presented in the model's definition. This is a complex work, mostly when it is expected that the agents' interactions correctly represent a human group behavior. In this work, we present a multi-agent system framework specifically designed to facilitate the implementation of dialogue games under the context of group decision-making in which agents interact as the humans do in face-to-face meetings. The proposed framework, named MAS4GDM, encapsulates the JADE framework and provides a layer that allows developers to easily implement their dialogue models without being concerned with some complex implementation details, such as: the communication model, the agents' life cycle, among others. We ran an experimental evaluation and verified that the proposed framework allows to implement dialogue models in an easier way and abstract the developers from important implementation details that can compromise the application's success.
... In order to empirically evaluate the proposed framework, we implemented an argumentation-based dialogue model [10,11] designed to the group decision-making context. In order to make the scenario more complex, the agents were defined with different social aspects: behavior styles, levels of expertise and credibility [12]. ...
Dialogue games have been applied to various contexts in computer science and artificial intelligence, particularly to define interactions between autonomous software agents. However, in order to implement dialogue games, the developers need to deal with other important details besides what is presented in the model’s definition. This is a complex work, mostly when it is expected that the agents’ interactions correctly represent a human group behavior. In this work, we present a multi-agent system framework specifically designed to facilitate the implementation of dialogue games under the context of group decision-making in which agents interact as the humans do in face-to-face meetings. The proposed framework, named MAS4GDM, encapsulates the JADE framework and provides a layer that allows developers to easily implement their dialogue models without being concerned with some complex implementation details, such as: the communication model, the agents’ life cycle, among others. We ran an experimental evaluation and verified that the proposed framework allows to implement dialogue models in an easier way and abstract the developers from important implementation details that can compromise the application’s success.
