Simulation of emotional behaviours for virtual agents with personalities

We have proposed an improved emotion model based on the well-known PAD model in affective computing and the NEO PI-R personality model in psychology. A novel parameter Emotion Intensity (EI) is proposed to represent different strength of anger, disgust, fear, happiness and sadness. Another novel Resistant formulation is also proposed to effectively simulate the complicated negative emotions. Eight experiments are conducted to simulate different emotional responses under different stimuli with different personality traits

Simulation of emotional behaviours for virtual agents with personalities

We have proposed an improved emotion model based on the well-known PAD model in affective computing and the NEO PI-R personality model in psychology. A novel parameter Emotion Intensity (EI) is proposed to represent different strength of anger, disgust, fear, happiness and sadness. Another novel Resistant formulation is also proposed to effectively simulate the complicated negative emotions. Eight experiments are conducted to simulate different emotional responses under different stimuli with different personality traits

Human emotion simulation in a dynamic environment

The aim of this work is to contribute to the believability of the simulated emotions for virtual entities to allow them display human like features. Endowing virtual entities with such features requires an appropriate architecture and model. For that, a study of emotional models from different perspective is undertaken. The fields include Psychology, Organic Components, Attention study and Computing. Two contributions are provided to reach the aim. The first one is a computational emotional model based on Scherer’s theory (K. Scherer, 2001). This contribution allows to generate a series of modifications in the affective state from one event by contrast to the existing solutions where one emotion is mapped to one single event. Several theories are used to make the model concrete. The second contribution make use of attention theories to build a paradigm in the execution of tasks in parallel. An algorithm is proposed to assess the available resources and allocate them to tasks for their execution. The algorithm is based on the multiple resources theory by Wickens (Wickens, 2008). The two contributions are combined into one architecture to produce a dynamic emotional system that allows its components to work in parallel. The first contribution was evaluated using a questionnaire. The results showed that mapping one event into a series of modifications in the affective state can enhance the believability of the simulation. The results also showed that people who develop more variations in the affective state are more perceived to be feminine

A model for providing emotion awareness and feedback using fuzzy logic in online learning

Monitoring users’ emotive states and using that information for providing feedback and scaffolding is crucial. In the learning context, emotions can be used to increase students’ attention as well as to improve memory and reasoning. In this context, tutors should be prepared to create affective learning situations and encourage collaborative knowledge construction as well as identify those students’ feelings which hinder learning process. In this paper, we propose a novel approach to label affective behavior in educational discourse based on fuzzy logic, which enables a human or virtual tutor to capture students’ emotions, make students aware of their own emotions, assess these emotions and provide appropriate affective feedback. To that end, we propose a fuzzy classifier that provides a priori qualitative assessment and fuzzy qualifiers bound to the amounts such as few, regular and many assigned by an affective dictionary to every word. The advantage of the statistical approach is to reduce the classical pollution problem of training and analyzing the scenario using the same dataset. Our approach has been tested in a real online learning environment and proved to have a very positive influence on students’ learning performance.Peer ReviewedPostprint (author's final draft

Affective Medicine: a review of Affective Computing efforts in Medical Informatics

Background: Affective computing (AC) is concerned with emotional interactions performed with and through computers. It is defined as “computing that relates to, arises from, or deliberately influences emotions”. AC enables investigation and understanding of the relation between human emotions and health as well as application of assistive and useful technologies in the medical domain. Objectives: 1) To review the general state of the art in AC and its applications in medicine, and 2) to establish synergies between the research communities of AC and medical informatics. Methods: Aspects related to the human affective state as a determinant of the human health are discussed, coupled with an illustration of significant AC research and related literature output. Moreover, affective communication channels are described and their range of application fields is explored through illustrative examples. Results: The presented conferences, European research projects and research publications illustrate the recent increase of interest in the AC area by the medical community. Tele-home healthcare, AmI, ubiquitous monitoring, e-learning and virtual communities with emotionally expressive characters for elderly or impaired people are few areas where the potential of AC has been realized and applications have emerged. Conclusions: A number of gaps can potentially be overcome through the synergy of AC and medical informatics. The application of AC technologies parallels the advancement of the existing state of the art and the introduction of new methods. The amount of work and projects reviewed in this paper witness an ambitious and optimistic synergetic future of the affective medicine field

A fuzzy-based approach for classifying students' emotional states in online collaborative work

(c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Emotion awareness is becoming a key aspect in collaborative work at academia, enterprises and organizations that use collaborative group work in their activity. Due to pervasiveness of ICT's, most of collaboration can be performed through communication media channels such as discussion forums, social networks, etc. The emotive state of the users while they carry out their activity such as collaborative learning at Universities or project work at enterprises and organizations influences very much their performance and can actually determine the final learning or project outcome. Therefore, monitoring the users' emotive states and using that information for providing feedback and scaffolding is crucial. To this end, automated analysis over data collected from communication channels is a useful source. In this paper, we propose an approach to process such collected data in order to classify and assess emotional states of involved users and provide them feedback accordingly to their emotive states. In order to achieve this, a fuzzy approach is used to build the emotive classification system, which is fed with data from ANEW dictionary, whose words are bound to emotional weights and these, in turn, are used to map Fuzzy sets in our proposal. The proposed fuzzy-based system has been evaluated using real data from collaborative learning courses in an academic context.Peer ReviewedPostprint (author's final draft