Emotions on agent based simulators for group formation

Time and space consuming are key factors in a meeting, and therefore must be object of consideration in any process of socialization. So, group decision simulation could be a valuable training tool, through which it will be possible to create and test virtual group decision scenarios. In this work we propose a multi-agent simulator of group decision making that models the participant cortex by considering its emotional states and the exchange of arguments among them.Fundação para a Ciência e a Tecnologia (FCT) - ArgEmotionAgents Project (POSI/EIA/56259/2004)

Simulating a team behaviour of affective agents using robocode

The study of the impact of emotion and affect in decision making processes involved in a working team stands for a multi-disciplinary issue (e.g. with insights from disciplines such as Psychology, Neuroscience, Philosophy and Computer Science). On the one hand, and in order to create such an environment we look at a team of affective agents to play into a battlefield, which present different emotional profiles (e.g. personality and mood).On the other hand, to attain cooperation, a voting mechanism and a decision-making process was implemented, being Robocode used as the simulation environment. Indeed, the results so far obtained are quite satisfying; the agent team performs quite well in the battlefield and undertakes different behaviours depending on the skirmish conditions.(undefined

In pursuit of satisfaction and the prevention of embarrassment : affective state in group recommender systems

Peer reviewedPostprin

ABS4GD: a multi-agent system that simulates group decision processes considering emotional and argumentative aspects

Emotion although being an important factor in our every day life it is many times forgotten in the development of systems to be used by persons. In this work we present an architecture for a ubiquitous group decision support system able to support persons in group decision processes. The system considers the emotional factors of the intervenient participants, as well as the argumentation between them. Particular attention will be taken to one of components of this system: the multi-agent simulator, modeling the human participants, considering emotional characteristics, and allowing the exchanges of hypothetic arguments among the participants

Normative Emotional Agents: a viewpoint paper

[EN] Human social relationships imply conforming to the norms, behaviors and cultural values of the society, but also socialization of emotions, to learn how to interpret and show them. In multiagent systems, much progress has been made in the analysis and interpretation of both emotions and norms. Nonetheless, the relationship between emotions and norms has hardly been considered and most normative agents do not consider emotions, or vice-versa. In this article, we provide an overview of relevant aspects within the area of normative agents and emotional agents. First we focus on the concept of norm, the different types of norms, its life cycle and a review of multiagent normative systems. Secondly, we present the most relevant theories of emotions, the life cycle of an agent¿s emotions, and how emotions have been included through computational models in multiagent systems. Next, we present an analysis of proposals that integrate emotions and norms in multiagent systems. From this analysis, four relationships are detected between norms and emotions, which we analyze in detail and discuss how these relationships have been tackled in the reviewed proposals. Finally, we present a proposal for an abstract architecture of a Normative Emotional Agent that covers these four norm-emotion relationships.This work was supported by the Spanish Government project TIN2017-89156- R, the Generalitat Valenciana project PROMETEO/2018/002 and the Spanish Goverment PhD Grant PRE2018-084940.Argente, E.; Del Val, E.; Pérez-García, D.; Botti Navarro, VJ. (2022). Normative Emotional Agents: a viewpoint paper. IEEE Transactions on Affective Computing. 13(3):1254-1273. https://doi.org/10.1109/TAFFC.2020.3028512S1254127313

From Affect Theoretical Foundations to Computational Models of Intelligent Affective Agents

[EN] The links between emotions and rationality have been extensively studied and discussed. Several computational approaches have also been proposed to model these links. However, is it possible to build generic computational approaches and languages so that they can be "adapted " when a specific affective phenomenon is being modeled? Would these approaches be sufficiently and properly grounded? In this work, we want to provide the means for the development of these generic approaches and languages by making a horizontal analysis inspired by philosophical and psychological theories of the main affective phenomena that are traditionally studied. Unfortunately, not all the affective theories can be adapted to be used in computational models; therefore, it is necessary to perform an analysis of the most suitable theories. In this analysis, we identify and classify the main processes and concepts which can be used in a generic affective computational model, and we propose a theoretical framework that includes all these processes and concepts that a model of an affective agent with practical reasoning could use. Our generic theoretical framework supports incremental research whereby future proposals can improve previous ones. This framework also supports the evaluation of the coverage of current computational approaches according to the processes that are modeled and according to the integration of practical reasoning and affect-related issues. This framework is being used in the development of the GenIA(3) architecture.This work is partially supported by the Spanish Government projects PID2020-113416RB-I00, GVA-CEICE project PROMETEO/2018/002, and TAILOR, a project funded by EU Horizon 2020 research and innovation programme under GA No 952215.Alfonso, B.; Taverner-Aparicio, JJ.; Vivancos, E.; Botti, V. (2021). From Affect Theoretical Foundations to Computational Models of Intelligent Affective Agents. Applied Sciences. 11(22):1-29. https://doi.org/10.3390/app112210874S129112

Context-aware emotion-based model for group decision making

Involving groups in important management processes such as decision making has several advantages. By discussing and combining ideas, counter ideas, critical opinions, identified constraints, and alternatives, a group of individuals can test potentially better solutions, sometimes in the form of new products, services, and plans. In the past few decades, operations research, AI, and computer science have had tremendous success creating software systems that can achieve optimal solutions, even for complex problems. The only drawback is that people don’t always agree with these solutions. Sometimes this dissatisfaction is due to an incorrect parameterization of the problem. Nevertheless, the reasons people don’t like a solution might not be quantifiable, because those reasons are often based on aspects such as emotion, mood, and personality. At the same time, monolithic individual decisionsupport systems centered on optimizing solutions are being replaced by collaborative systems and group decision-support systems (GDSSs) that focus more on establishing connections between people in organizations. These systems follow a kind of social paradigm. Combining both optimization- and socialcentered approaches is a topic of current research. However, even if such a hybrid approach can be developed, it will still miss an essential point: the emotional nature of group participants in decision-making tasks. We’ve developed a context-aware emotion based model to design intelligent agents for group decision-making processes. To evaluate this model, we’ve incorporated it in an agent-based simulator called ABS4GD (Agent-Based Simulation for Group Decision), which we developed. This multiagent simulator considers emotion- and argument based factors while supporting group decision-making processes. Experiments show that agents endowed with emotional awareness achieve agreements more quickly than those without such awareness. Hence, participant agents that integrate emotional factors in their judgments can be more successful because, in exchanging arguments with other agents, they consider the emotional nature of group decision making

Positive Versus Negative Agents: The Effects of Emotions on Learning

The current study investigates the impact of affect, mood contagion, and linguistic alignment on learning during tutorial conversations between a human student and two artificial pedagogical agents. The study uses an Intelligent Tutoring System known as OperationARIES! to engage students in tutorial conversations with animated agents. In this investigation, 48 college students (N = 48) conversed with pedagogical agents as they displayed 3 different moods (i.e., positive, negative, and neutral) along with a control condition in a within-subjects design. Results indicate that the mood of the agent did not significantly impact student learning even though mood contagion did occur between the artificial agent and the human student. Learning was influenced by the student\u27s self-reported arousal level and the alignment scores that reflected a shared mental representation between the human student and the artificial agents. The results suggest that arousal and linguistic alignment during the tutorial conversations may play a role in learning

Robotic Psychology. What Do We Know about Human-Robot Interaction and What Do We Still Need to Learn?

“Robotization”, the integration of robots in human life will change human life drastically. In many situations, such as in the service sector, robots will become an integrative part of our lives. Thus, it is vital to learn from extant research on human-robot interaction (HRI). This article introduces robotic psychology that aims to bridge the gap between humans and robots by providing insights into particularities of HRI. It presents a conceptualization of robotic psychology and provides an overview of research on service-focused human-robot interaction. Theoretical concepts, relevant to understand HRI with are reviewed. Major achievements, shortcomings, and propositions for future research will be discussed