Simplifying the Design of Human-Like Behaviour

2010 ◽  
Vol 1 (1) ◽  
pp. 30-50 ◽  
Author(s):  
Joanna J. Bryson ◽  
Emmanuel Tanguy
Keyword(s):  
Emotional Expression ◽  
Autonomous Agents ◽  
Emotional State ◽  
Emotional Responses ◽  
Full Time ◽  
Emotional States ◽  
Virtual Character ◽  
Agent Behavior ◽  
Emotion Representation ◽  
Representational Framework

Human intelligence requires decades of full-time training before it can be reliably utilized in modern economies. In contrast, AI agents must be made reliable but interesting in relatively short order. Realistic emotion representations are one way to ensure that even relatively simple specifications of agent behavior will be expressed with engaging variation, and those social and temporal contexts can be tracked and responded to appropriately. We describe a representation system for maintaining an interacting set of durative states to replicate emotional control. Our model, the Dynamic Emotion Representation (DER), integrates emotional responses and keeps track of emotion intensities changing over time. The developer can specify an interacting network of emotional states with appropriate onsets, sustains, and decays. The levels of these states can be used as input for action selection, including emotional expression. We present both a general representational framework and a specific instance of a DER network constructed for a virtual character. The character’s DER uses three types of emotional state as classified by duration timescales, keeping with current emotional theory. We demonstrate the system with a virtual actor. We also demonstrate how even a simplified version of this representation can improve goal arbitration in autonomous agents.

Simplifying the Design of Human-Like Behaviour

2012 ◽  
pp. 32-53
Author(s):  
Joanna J. Bryson ◽  
Emmanuel Tanguy
Keyword(s):  
Autonomous Agents ◽  
Emotional Responses ◽  
Human Intelligence ◽  
Full Time ◽  
Emotional States ◽  
Virtual Character ◽  
Specific Instance ◽  
Emotion Representation ◽  
Representational Framework ◽  
Over Time

Human intelligence requires decades of full-time training before it can be reliably utilised in modern economies. In contrast, AI agents must be made reliable but interesting in relatively short order. Realistic emotion representations are one way to ensure that even relatively simple specifications of agent behaviour will be expressed with engaging variation, and those social and temporal contexts can be tracked and responded to appropriately. We describe a representation system for maintaining an interacting set of durative states to replicate emotional control. Our model, the Dynamic Emotion Representation (DER), integrates emotional responses and keeps track of emotion intensities changing over time. The developer can specify an interacting network of emotional states with appropriate onsets, sustains and decays. The levels of these states can be used as input for action selection, including emotional expression. We present both a general representational framework and a specific instance of a DER network constructed for a virtual character. The character’s DER uses three types of emotional state as classified by duration timescales, keeping with current emotional theory. We demonstrate the system with a virtual actor. We also demonstrate how even a simplified version of this representation can improve goal arbitration in autonomous agents.

scholarly journals An agent-based model of opinion polarization driven by emotions

2020 ◽  
Author(s):  
Frank Schweitzer ◽  
Tamas Krivachy ◽  
David Garcia
Keyword(s):  
Emotional Expression ◽  
Emotional State ◽  
Critical Conditions ◽  
Emotional States ◽  
Agent Based Model ◽  
Emotional Information ◽  
Agent Based ◽  
Non Linear ◽  
Agent Based Simulations ◽  
Linear Manner

We provide an agent-based model to explain the emergence of collective opinions not based on feedback between different opinions, but based on emotional interactions between agents. The driving variable is the emotional state of agents, characterized by their valence, quantifying the emotion from unpleasant to pleasant, and their arousal, quantifying the degree of activity associated with the emotion. Both determine their emotional expression, from which collective emotional information is generated. This information feeds back on the dynamics of emotional states and of individual opinions in a non-linear manner. We derive the critical conditions for emotional interactions to obtain either consensus or polarization of opinions. Stochastic agent-based simulations and formal analyses of the model explain our results. Possible ways to validate the model are discussed.

scholarly journals An Agent-Based Model of Opinion Polarization Driven by Emotions

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Frank Schweitzer ◽  
Tamas Krivachy ◽  
David Garcia
Keyword(s):  
Emotional Expression ◽  
Emotional State ◽  
Critical Conditions ◽  
Emotional States ◽  
Agent Based Model ◽  
Emotional Information ◽  
Agent Based ◽  
Agent Based Simulations

We provide an agent-based model to explain the emergence of collective opinions not based on feedback between different opinions, but based on emotional interactions between agents. The driving variable is the emotional state of agents, characterized by their valence, quantifying the emotion from unpleasant to pleasant, and their arousal, quantifying the degree of activity associated with the emotion. Both determine their emotional expression, from which collective emotional information is generated. This information feeds back on the dynamics of emotional states and individual opinions in a nonlinear manner. We derive the critical conditions for emotional interactions to obtain either consensus or polarization of opinions. Stochastic agent-based simulations and formal analyses of the model explain our results. Possible ways to validate the model are discussed.

Effect of Emotions in a Lexical Decision Task

2017 ◽  
Vol 76 (2) ◽  
pp. 71-79 ◽  
Author(s):  
Hélène Maire ◽  
Renaud Brochard ◽  
Jean-Luc Kop ◽  
Vivien Dioux ◽  
Daniel Zagar
Keyword(s):  
Lexical Decision ◽  
Lexical Decision Task ◽  
Emotional State ◽  
Control Group ◽  
Decision Task ◽  
Attentional Orienting ◽  
Emotional States ◽  
Sequence Effect ◽  
Emotion Induction ◽  
Before And After

Abstract. This study measured the effect of emotional states on lexical decision task performance and investigated which underlying components (physiological, attentional orienting, executive, lexical, and/or strategic) are affected. We did this by assessing participants’ performance on a lexical decision task, which they completed before and after an emotional state induction task. The sequence effect, usually produced when participants repeat a task, was significantly smaller in participants who had received one of the three emotion inductions (happiness, sadness, embarrassment) than in control group participants (neutral induction). Using the diffusion model ( Ratcliff, 1978 ) to resolve the data into meaningful parameters that correspond to specific psychological components, we found that emotion induction only modulated the parameter reflecting the physiological and/or attentional orienting components, whereas the executive, lexical, and strategic components were not altered. These results suggest that emotional states have an impact on the low-level mechanisms underlying mental chronometric tasks.

scholarly journals Measuring Farm Animal Emotions—Sensor-Based Approaches

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 553
Author(s):  
Suresh Neethirajan ◽  
Inonge Reimert ◽  
Bas Kemp
Keyword(s):  
Emotional Expression ◽  
Emotional Responses ◽  
Facial Feature ◽  
Agricultural Technology ◽  
Farm Animals ◽  
Expression Recognition ◽  
Biometric Data ◽  
Challenges And Opportunities ◽  
Expression Of Emotions ◽  
Expression Measurement

Understanding animal emotions is a key to unlocking methods for improving animal welfare. Currently there are no ‘benchmarks’ or any scientific assessments available for measuring and quantifying the emotional responses of farm animals. Using sensors to collect biometric data as a means of measuring animal emotions is a topic of growing interest in agricultural technology. Here we reviewed several aspects of the use of sensor-based approaches in monitoring animal emotions, beginning with an introduction on animal emotions. Then we reviewed some of the available technological systems for analyzing animal emotions. These systems include a variety of sensors, the algorithms used to process biometric data taken from these sensors, facial expression, and sound analysis. We conclude that a single emotional expression measurement based on either the facial feature of animals or the physiological functions cannot show accurately the farm animal’s emotional changes, and hence compound expression recognition measurement is required. We propose some novel ways to combine sensor technologies through sensor fusion into efficient systems for monitoring and measuring the animals’ compound expression of emotions. Finally, we explore future perspectives in the field, including challenges and opportunities.

scholarly journals Dogs can infer implicit information from human emotional expressions

2021 ◽  
Author(s):  
Natalia Albuquerque ◽  
Daniel S. Mills ◽  
Kun Guo ◽  
Anna Wilkinson ◽  
Briseida Resende
Keyword(s):  
Emotional State ◽  
Food Resource ◽  
Emotional Expressions ◽  
Emotional States ◽  
Emotional Information ◽  
Implicit Information ◽  
Emotional Display ◽  
Affective Information ◽  
Social Decision Making ◽  
Current Behaviour

AbstractThe ability to infer emotional states and their wider consequences requires the establishment of relationships between the emotional display and subsequent actions. These abilities, together with the use of emotional information from others in social decision making, are cognitively demanding and require inferential skills that extend beyond the immediate perception of the current behaviour of another individual. They may include predictions of the significance of the emotional states being expressed. These abilities were previously believed to be exclusive to primates. In this study, we presented adult domestic dogs with a social interaction between two unfamiliar people, which could be positive, negative or neutral. After passively witnessing the actors engaging silently with each other and with the environment, dogs were given the opportunity to approach a food resource that varied in accessibility. We found that the available emotional information was more relevant than the motivation of the actors (i.e. giving something or receiving something) in predicting the dogs’ responses. Thus, dogs were able to access implicit information from the actors’ emotional states and appropriately use the affective information to make context-dependent decisions. The findings demonstrate that a non-human animal can actively acquire information from emotional expressions, infer some form of emotional state and use this functionally to make decisions.

An experimental study on the effect of emotion lines in comics

Semiotica ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Amitash Ojha ◽  
Charles Forceville ◽  
Bipin Indurkhya
Keyword(s):  
Experimental Study ◽  
Emotional State ◽  
Negative Emotions ◽  
Emotional States ◽  
One To One

Abstract Both mainstream and art comics often use various flourishes surrounding characters’ heads. These so-called “pictorial runes” (also called “emanata”) help convey the emotional states of the characters. In this paper, using (manipulated) panels from Western and Indian comic albums as well as neutral emoticons and basic shapes in different colors, we focus on the following two issues: (a) whether runes increase the awareness in comics readers about the emotional state of the character; and (b) whether a correspondence can be found between the types of runes (twirls, spirals, droplets, and spikes) and specific emotions. Our results show that runes help communicate emotion. Although no one-to-one correspondence was found between the tested runes and specific emotions, it was found that droplets and spikes indicate generic emotions, spirals indicate negative emotions, and twirls indicate confusion and dizziness.

Emotional Mechanisms Linking Incivility at Work to Aggression and Withdrawal at Home: An Experience-Sampling Study

2016 ◽  
Vol 44 (7) ◽  
pp. 2888-2908 ◽  
Author(s):  
Sandy Lim ◽  
Remus Ilies ◽  
Joel Koopman ◽  
Paraskevi Christoforou ◽  
Richard D. Arvey
Keyword(s):  
Experience Sampling ◽  
Workplace Incivility ◽  
Full Time ◽  
Emotional States ◽  
Mediation Model ◽  
Moderated Mediation Model ◽  
High Trait ◽  
The Relationship ◽  
At Home ◽  
Trait Hostility

We report an experience-sampling study examining the spillover of workplace incivility on employees’ home lives. Specifically, we test a moderated mediation model whereby discrete emotions transmit the effects of workplace incivility to specific family behaviors at home. Fifty full-time employees from southeast Asia provided 363 observations over a 10-day period on workplace incivility and various emotional states. Daily reports of employees’ marital behaviors were provided by the spouses each evening. Results showed that state hostility mediated the link from workplace incivility to increased angry and withdrawn marital behaviors at home. Also, trait hostility served as a moderator such that the relationship between workplace incivility and hostile emotions was stronger for employees with high trait hostility.

scholarly journals The role of emotional service in the restaurant business

2022 ◽  
pp. 164-167
Author(s):  
N. A. Ofitserova
Keyword(s):  
Emotional Intelligence ◽  
Customer Satisfaction ◽  
Emotional Labor ◽  
Work Performance ◽  
Positive Emotions ◽  
Emotional State ◽  
Communicative Competence ◽  
Point Of View ◽  
Emotional States

The article considers the restaurant business from the point of view of not only the entrepreneurial aspect, but also the service aspect, which is fundamental. The reasons why people visit restaurants have been revealed. In addition to physical need, restaurants are an element of cognition and a way of experiencing positive emotions. The importance of the restaurant business in shaping people’s positive emotional state has been formulated. Two forms of emotional labor of an employee and the influence of emotional states on work performance have been highlighted. The role of emotional intelligence and communicative competence in customer satisfaction with a restaurant visit has been determined. The importance of developing emotional intelligence has been concluded. Recommendations for its development has been formulated. 

scholarly journals Speech-based human emotion recognition

2021 ◽  
Author(s):  
Talieh Seyed Tabtabae
Keyword(s):  
Emotion Recognition ◽  
Emotional State ◽  
Speaker Identification ◽  
Recognition System ◽  
Research Area ◽  
Speech Signals ◽  
Spectral Features ◽  
Emotional States ◽  
Human Communication ◽  
Set Up

Automatic Emotion Recognition (AER) is an emerging research area in the Human-Computer Interaction (HCI) field. As Computers are becoming more and more popular every day, the study of interaction between humans (users) and computers is catching more attention. In order to have a more natural and friendly interface between humans and computers, it would be beneficial to give computers the ability to recognize situations the same way a human does. Equipped with an emotion recognition system, computers will be able to recognize their users' emotional state and show the appropriate reaction to that. In today's HCI systems, machines can recognize the speaker and also content of the speech, using speech recognition and speaker identification techniques. If machines are equipped with emotion recognition techniques, they can also know "how it is said" to react more appropriately, and make the interaction more natural. One of the most important human communication channels is the auditory channel which carries speech and vocal intonation. In fact people can perceive each other's emotional state by the way they talk. Therefore in this work the speech signals are analyzed in order to set up an automatic system which recognizes the human emotional state. Six discrete emotional states have been considered and categorized in this research: anger, happiness, fear, surprise, sadness, and disgust. A set of novel spectral features are proposed in this contribution. Two approaches are applied and the results are compared. In the first approach, all the acoustic features are extracted from consequent frames along the speech signals. The statistical values of features are considered to constitute the features vectors. Suport Vector Machine (SVM), which is a relatively new approach in the field of machine learning is used to classify the emotional states. In the second approach, spectral features are extracted from non-overlapping logarithmically-spaced frequency sub-bands. In order to make use of all the extracted information, sequence discriminant SVMs are adopted. The empirical results show that the employed techniques are very promising.

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