Affective Cognitive Modeling for Autonomous Agents Based on Scherer’s Emotion Theory

Lisetti, Christine L.; Marpaung, Andreas H.

doi:10.1007/978-3-540-69912-5_3

Cited by 7 publications

(5 citation statements)

References 16 publications

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“…Scherer in his relatively recent paper [52] discusses the SEC approach within the context of three levels of emotion processing, as also suggested by Leventhal [23], which underlies the design of our affective-cognitive architecture [38], [30]:…”

Section: Monitoring Of Internal State and Environment Which Is Relatementioning

confidence: 99%

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Toward multimodal fusion of affective cues

Paleari

Lisetti

2006

Proceedings of the 1st ACM International Workshop on Human-Centered Multimedia

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During face to face communication, it has been suggested that as much as 70% of what people communicate when talking directly with others is through paralanguage involving multiple modalities combined together (e.g. voice tone and volume, body language). In an attempt to render humancomputer interaction more similar to human-human communication and enhance its naturalness, research on sensory acquisition and interpretation of single modalities of human expressions have seen ongoing progress over the last decade. These progresses are rendering current research on artificial sensor fusion of multiple modalities an increasingly important research domain in order to reach better accuracy of congruent messages on the one hand, and possibly to be able to detect incongruent messages across multiple modalities (incongruency being itself a message about the nature of the information being conveyed). Accurate interpretation of emotional signals -quintessentially multimodal -would hence particularly benefit from multimodal sensor fusion and interpretation algorithms. In this paper we provide a state of the art multimodal fusion and describe one way to implement a generic framework for multimodal emotion recognition. The system is developed within the MAUI framework [31] and Scherer's Component Process Theory (CPT) [49,50,51,24,52], with the goal to be modular and adaptive. We want the designed framework to be able to accept different single and multi modality recognition systems and to automatically adapt the fusion algorithm to find optimal solutions. The system also aims to be adaptive to channel (and system) reliability.

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Section: Monitoring Of Internal State and Environment Which Is Relatementioning

confidence: 99%

“…ASIA (Affective Social Intelligent Architecture) is based on a BDI+E (Belief, Desire, Intention + Emotion) architecture (described by Lisetti and et al [30]). BDI+E couples the well known Belief Desire and Intention paradigm, [46,47] to Emotion capabilities at different levels.…”

Section: Affective Social Intelligent Architecturementioning

confidence: 99%

Toward multimodal fusion of affective cues

Paleari

Lisetti

2006

Proceedings of the 1st ACM International Workshop on Human-Centered Multimedia

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“…communication can be used to convey human intent, non-verbal communication has recently been deemed to be more meaningful than verbal content, especially in demonstrating changes in a person's state [47]. To date, a great deal of work has been conducted on developing automated techniques utilized in determining emotions through facial expressions including Bayesian networks [48,49], optical flow techniques [50], artificial neural networks [51], and fuzzy logic [52]. An overview of proposed techniques can be found in [53].…”

Section: Human Body Pose Classification Techniquesmentioning

confidence: 99%

Human Body Pose Interpretation and Classification for Social Human-Robot Interaction

McColl¹,

Zhang

Nejat

2011

Int J of Soc Robotics

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A novel breed of robots known as socially assistive robots is emerging. These robots are capable of providing assistance to individuals through social and cognitive interaction. However, there are a number of research issues that need to be addressed in order to design such robots. In this paper, we address one main challenge in the development of intelligent socially assistive robots: The robot's ability to identify human non-verbal communication during assistive interactions. In particular, we present a unique non-contact and non-restricting automated sensor-based approach for identification and categorization of human upper body language in determining how accessible a person is to the robot during natural real-time human-robot interaction (HRI). This classification will allow a robot to effectively determine its own reactive task-driven behavior dur-

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“…2) le deuxième niveau schématique prend en compte de façon beaucoup plus sophistiquée l'apprentissage et la mémoire des expériences émotionnelles associées à certaines situations, l'organisation et le rappel de la connaissance avec un système de représentation schématique (Rumelhart, 1980;Rumelhart et al, 1986) (et dont nous avons implémenté deux prototypes (Murphy et al, 2002;Lisetti et al, 2006a) sur plateformes robotiques) ;…”

Section: Le Paradigme Maui : Interfaces Multimodales Affectives Pour unclassified

Le paradigme MAUI pour des agents multimodaux d'interface homme-machine socialement intelligents

Lisetti¹

2006

Revue d'intelligence artificielle

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Dans cet article, nous exposons notre programme de recherche ou paradigme en informatique bio-affective-sociale pour guider et participer au développement d'agents multimodaux socialement intelligents en termes de : (1) prise de décision et de (2) communication. Nous présentons certains de nos résultats traitant d'une partie des composants décrits dans ce paradigme que nous avons choisi d'explorer. En particulier, nous travaillons sur la reconnaissance des émotions à partir de signaux physiologiques, sur la construction de modèles des émotions de l'utilisateur basée entre autres sur la reconnaissance des émotions, l'élaboration d'architecture pour agents socialement intelligents capables d'utiliser le modèle des émotions de l'utilisateur pour interagir de façon plus intuitive avec cet utilisateur dans diverses applications et contextes.

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Affective Cognitive Modeling for Autonomous Agents Based on Scherer’s Emotion Theory

Cited by 7 publications

References 16 publications

Toward multimodal fusion of affective cues

Toward multimodal fusion of affective cues

Human Body Pose Interpretation and Classification for Social Human-Robot Interaction

Le paradigme MAUI pour des agents multimodaux d'interface homme-machine socialement intelligents

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