Modeling Personality, Mood, and Emotions

Zhang, Juzheng; Zheng, Jianmin; Thalmann, Nadia Magnenat

doi:10.1007/978-3-319-19947-4_10

Cited by 10 publications

(3 citation statements)

References 37 publications

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“…Our model draws on [29], considering mood as in Zhang et al [99] and emotion as in Lövheim [54], but with subtle variations. Nine mood states are situated in a bidimensional valence-arousal space where the neutral mood is situated at the origin of both axes.…”

Section: Psychologymentioning

confidence: 99%

A motivational model based on artificial biological functions for the intelligent decision-making of social robots

et al. 2023

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Modelling the biology behind animal behaviour has attracted great interest in recent years. Nevertheless, neuroscience and artificial intelligence face the challenge of representing and emulating animal behaviour in robots. Consequently, this paper presents a biologically inspired motivational model to control the biological functions of autonomous robots that interact with and emulate human behaviour. The model is intended to produce fully autonomous, natural, and behaviour that can adapt to both familiar and unexpected situations in human–robot interactions. The primary contribution of this paper is to present novel methods for modelling the robot’s internal state to generate deliberative and reactive behaviour, how it perceives and evaluates the stimuli from the environment, and the role of emotional responses. Our architecture emulates essential animal biological functions such as neuroendocrine responses, circadian and ultradian rhythms, motivation, and affection, to generate biologically inspired behaviour in social robots. Neuroendocrinal substances control biological functions such as sleep, wakefulness, and emotion. Deficits in these processes regulate the robot’s motivational and affective states, significantly influencing the robot’s decision-making and, therefore, its behaviour. We evaluated the model by observing the long-term behaviour of the social robot Mini while interacting with people. The experiment assessed how the robot’s behaviour varied and evolved depending on its internal variables and external situations, adapting to different conditions. The outcomes show that an autonomous robot with appropriate decision-making can cope with its internal deficits and unexpected situations, controlling its sleep–wake cycle, social behaviour, affective states, and stress, when acting in human–robot interactions.

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Section: Psychologymentioning

confidence: 99%

A motivational model based on artificial biological functions for the intelligent decision-making of social robots

et al. 2023

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“…When analyzing the impact of artificial agents on humans, there are now decades of studies focused on Human-Robot [29] and Human-Computer [30] Interaction (HRI and HCI, respectively). Social and affective computing research suggests humans tend to treat computers as social actors, [31] where attributes such as personality and emotions are modeled to affect how these agents are perceived [32,33].…”

Section: Human-centric Analysis Of Rlmentioning

confidence: 99%

Incorporating rivalry in reinforcement learning for a competitive game

Barros

Tanevska

Yalçın

et al. 2022

Neural Comput & Applic

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Recent advances in reinforcement learning with social agents have allowed such models to achieve human-level performance on certain interaction tasks. However, most interactive scenarios do not have performance alone as an end-goal; instead, the social impact of these agents when interacting with humans is as important and largely unexplored. In this regard, this work proposes a novel reinforcement learning mechanism based on the social impact of rivalry behavior. Our proposed model aggregates objective and social perception mechanisms to derive a rivalry score that is used to modulate the learning of artificial agents. To investigate our proposed model, we design an interactive game scenario, using the Chef’s Hat Card Game, and examine how the rivalry modulation changes the agent’s playing style, and how this impacts the experience of human players on the game. Our results show that humans can detect specific social characteristics when playing against rival agents when compared to common agents, which affects directly the performance of the human players in subsequent games. We conclude our work by discussing how the different social and objective features that compose the artificial rivalry score contribute to our results.

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“…Emotions and mood are important elements in affective computing. While emotions are generally expressed using instantaneous behaviors, such as facial expressions or gestures, moods are longer-lived and affect human behaviors [8]. A well-known model used in affective computing for encoding both emotions and moods is the PAD (Pleasure-Arousal-Dominance) dimensional model [9], [10].…”

Section: Related Work and Contributionsmentioning

confidence: 99%

Adaptive Fuzzy and Predictive Controllers for Expressive Robot Arm Movement during Human and Environment Interaction

Rincon¹,

Coronado²,

Hendra³

et al. 2019

ijmerr

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To create robots able to generate expressive motions and improve human robot interaction (HRI). An innovative adaptive control system architecture for a robot arm is developed which can adapt the control parameters and motion trajectories according to the perception generated by the human, the environment, and the overall robot interaction. An adaptive fuzzy controller that maps environmental and HRI factors to the PAD emotional model (Pleasure, Arousal, and Dominance) is proposed. These PAD values are used to change the robot strategy to generate trajectories and control parameters, which are designed to express different emotional states. The robot motions are commanded by the Robust Generalized Predictive Controllers (RGPC), using optimization by Youla parameters, that involves robot regulation with adaptive motion. The optimization control uses an adaptive receding horizon designed according to the response of the human and environment interaction. This proposal allows to generate motions with more personalized characteristics for human robot interaction in a non-humanoid robots.  Index Terms-robot arm, adaptive fuzzy control, adaptive robust predictive control, affective robotics I. INTRODUCTION Robots with the ability to cooperate with people and interact in social contexts are now being used in diverse scenarios. To enable long-term interactions, these robots must be able to convey a sense of believability, diversity and adaptation to environmental conditions [1]. It is often said that a key factor in creating intelligent social agents is the ability to express different affective states [2]. However, even when a large portion of human-human affective communication is expressed with gestures and postures [3], most studies related to emotional expression in robotics focused in artificially reproduced human-like facial expressions. Two notable examples are [4] and [5]. Moreover, many robots available on the market cannot perform facial expression due to the mechanical or technological limitations. Examples of these types of robots are mobile robots, manipulator robots, and quadropters, among others. Literature in affective computing reports very few works addressing the challenges that imply the use of non-zoomorphic robots,

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Modeling Personality, Mood, and Emotions

Cited by 10 publications

References 37 publications

A motivational model based on artificial biological functions for the intelligent decision-making of social robots

A motivational model based on artificial biological functions for the intelligent decision-making of social robots

Incorporating rivalry in reinforcement learning for a competitive game

Adaptive Fuzzy and Predictive Controllers for Expressive Robot Arm Movement during Human and Environment Interaction

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