Indiana University-Purdue University Indianapolis (IUPUI) / Just as physical appearance affects persuasion and compliance in human communication, it may also bias the processing of information from avatars, computer-animated characters, and other computer interfaces with faces. Although the most persuasive of these interfaces are often the most humanlike, they incur the greatest risk of falling into the uncanny valley, the loss of empathy associated with eerily human characters. The uncanny valley could delay the acceptance of humanlike interfaces in everyday roles. To determine the extent to which the uncanny valley affects persuasion, two experiments were conducted online with undergraduates from Indiana University. The first experiment (N = 426) presented an ethical dilemma followed by the advice of an authority figure. The authority was manipulated in three ways: depiction (recorded or animated), motion quality (smooth or jerky), and recommendation (disclose or refrain from disclosing sensitive information). Of these, only the recommendation changed opinion about the dilemma, even though the animated depiction was eerier than the human depiction. These results indicate that compliance with an authority persists even when using a realistic computer-animated double. The second experiment (N = 311) assigned one of two different dilemmas in professional ethics involving the fate of a humanlike character. In addition to the dilemma, there were three manipulations of the character’s human realism: depiction (animated human or humanoid robot), voice (recorded or synthesized), and motion quality (smooth or jerky). In one dilemma, decreasing depiction realism or increasing voice realism increased eeriness. In the other dilemma, increasing depiction realism decreased perceived competence. However, in both dilemmas realism had no significant effect on whether to punish the character. Instead, the willingness to punish was predicted in both dilemmas by narratively characterized trustworthiness. Together, the experiments demonstrate both direct and indirect effects of narratives on responses to humanlike interfaces. The effects of human realism are inconsistent across different interactions, and the effects of the uncanny valley may be suppressed through narrative characterization.

<p>Crowd simulations have been a part of crowd research since the 1980s. This document focuses on understanding how study participants interact and perceive a virtual crowd in an immersive virtual environment. Specifically, avoidance proximity variations (i.e., low, medium, and high avoidance proximity [defined as avoidance radius]) were explored when assigned to crowd agents impacted participants’ interaction with the virtual crowd. During the study, participants were instructed to walk in a virtual environment. At the same time, a virtual crowd was scripted to move toward participants’ starting position following a straight path. During the participants’ walking task, movement data was collected (i.e., trajectory length and completion time) and immediately after each experimental condition, participants were asked to self-report their experience (i.e., presence, co-presence, behavioral independence, crowd realism, crowd interaction realism, perceived politeness, and emotional reactivity). Based on the collected data, it was found that when participants were exposed to the high avoidance proximity condition, they: 1) followed longer paths, 2) spent more time reaching the target goal, 3) rated the virtual crowd less polite, 4) rated the virtual crowd and their interaction with the virtual crowd less realistic, 5) rated the behavior independence of the virtual crowd lower, 6) self-reported higher emotional reactivity, and 7) positive correlations were found between trajectory length and behavioral independence, trajectory length and crowd interaction realism, and completion time and perceived politeness. Suggestions for further research on human-virtual crowd interaction are also discussed.</p>

Simulation models that replicate realistic crowd behaviours and dynamics are of great societal use in a variety of fields of research and entertainment. In order to evaluate the accuracy of such models there is a demand for metrics and evaluation solutions that measure how well they simulate the dynamics of real crowds. A crowd similarity metric is a performance indicator which quantifies the similarity of crowd trajectories. Similarity metrics may be used to evaluate the validity of simulation models by comparing the content they produce to real-world crowd trajectory data. This thesis presents and evaluates a similarity metric which employs an approach based on the Sum of Absolute Differences to compare two-dimensional crowd trajectories. The metric encapsulates the similarity of crowd trajectories by iteratively summing time-wise positional differences on a per-agent basis. The resulting metric is simple, highly reproducible and simulatorindependent. Its accuracy in quantifying similarity is evaluated by means of a user study investigating the correlation between metric values and human perception of similarity for real and simulated crowd scenarios of varying density, trajectory, speed, and presence of environmental obstacles. The user study explores different aspects of crowd perception by dividing similarity ratings on a five-point Likert scale into four categories: overall, in terms of trajectories, speeds, and positions. Scenarios and rating categories that indicate high and low degrees of correlation between metric values and perceived similarity are identified and discussed. Furthermore, the findings are compared to previous research on crowd trajectory similarity metrics. The results indicate that the metric shows promising potential for accurate similarity measurement in simple and sparse scenarios across all rated categories. Moreover, the metric is strongly correlated with the trajectory-ratings of crowd motion similarity. However, it appears to not correlate well with the perception of overall similarity for large and dense crowds. / Simuleringsmodeller som efterhärmar realistiskt beteende och dynamik bland folkmassor är av stor samhällelig nytta i flertalet forskningsområden och i underhållningsbranschen. För att utvärdera sådana modellers noggrannhet finns det en efterfrågan av metriker och bedömningslösningar som mäter hur väl modeller simulerar verklig folkmassadynamik. En likhetsmetrik för folkmassautvärdering är en prestationsindikator som kvantifierar likheten mellan folkmassarörelser. Likhetsmetriker kan användas för att utvärdera simuleringsmodellers validitet genom att jämföra beteendet de producerar med rörelsedata från verkliga folkmassor. Följande examensarbete presenterar och utvärderar en likhetsmetrik för folkmassor som utnyttjar en metod baserad på ”Summan av Absoluta Skillnader” för att jämföra par av två-dimensionella folkmassarörelser. Metriken uppskattar likheten mellan två folkmassors rörelser genom att iterativt summera skillnaderna mellan folkmassornas positioner baserat på de individuella virtuella agenterna. Resultatet är en simpel, kraftigt reproducerbar och simulator-oberoende metrik. Dess noggrannhet avseende likhetsestimering utvärderas med en perceptuell användarstudie som undersöker korrelationen mellan metrikvärden och mänsklig perception av visuell likhet för flertalet verkliga och simulerade folkmassor av varierande densitet, färdväg, hastighet, och förekomst av hinder. Användarstudien utforskar olika aspekter av folkmassaperception genom att dela upp likhetsgradering på en femgradig skala i fyra kategorier: övergripande, avseende färdvägar, hastigheter, och positioner. Folkmassascenarier och graderingskategorier som indikerar höga och låga nivåer av korrelation mellan metrikvärden och uppfattad likhet identifieras och diskuteras. Fortsättningsvis jämförs resultaten med tidigare forskning om likhetsmetriker för folkmassautvärdering. Resultaten tyder på att metriken har lovande potential för noggrann likhetsestimering i enkla och glesa scenarier oavsett graderingskategori. Dessutom korrelerar metriken starkt med färdvägs-graderingar av likhet. Däremot verkar den inte korrelera väl med perceptionen av övergripande likhet för stora och täta folkmassor

En el ámbito de la Vida Artificial, el termino de agentes virtuales autónomos (AVA) es utilizado para definir personajes que habitan en un mundo virtual 3D, y cuya apariencia y comportamiento intentan imitar las de seres vivientes reales, con la intención de crear la ilusión de que ellos también lo son. Pero para que un personaje virtual resulte convincente, no solo debe parecer real, sino también comportarse de manera real. El presente trabajo hace un aporte al desarrollo de sistemas de animación del comportamiento para AVAs, enfocándose principalmente en la animación de humanos virtuales autónomos; presentando un marco general de simulacion para la incorporacion de tecnicas computacionales que faliciten el desarrollo de simulaciones de actores virtuales, a la vez que introduce un nuevo esquema de animacion del comportamiento, el cual incorpora diferentes modulos que intentan emular el proceso cognitivo que genera la toma de desiciones. La integracion de los elentos graficos y el sistema de control de comportamiento desarrollado dan origen a la generacion de simulaciones donde humanos virtuales exhiben comportamientos realistas, basados en objetivos, y motivados por parametros emocionales, estados internos, y conocimiento sobre el entorno y sobre si mismos. / One of the most exciting topics in Computer Graphics is the realistic animation of the behavior of virtual agents. In this field (also known as Artificial Life), the challenge is to create a behavioral system for the virtual agents so that they behave as realistic as possible. To this aim, a number of different techniques have been applied during the last few years. Among them, the most powerful and appealing ones seem to be those based on Artifcial Intelligence (AI) techniques. After all, most of the AI techniques (such as neural networks or expert systems) are based on the idea of reproducing the structure and behavior of the human brain. Consequently, they are optimal candidates to be applied to this goal. This work is focused on describing a general framework for simulating the behavior of virtual actors evolving within a virtual 3D world, and achive realistically to simulate the mental processes of those virtual agents and the application of some well-known AI techniques to this purpose. In particular, we explore some of these techniques and discuss how can they be successfully applied to tackle this issue. For each technique, a discussion about how can it be applied to perform specific tasks is given. We also describe how can some mental processes such as memory, reasonning, recognition, perception and others be realistically simulated by using a carefully chosen combination of these techniques.

Embodied conversational agents put a “human” touch on intelligent tutoring systems by using conversation to support learning. When considering instruction in interpersonal domains, such as intercultural negotiation, the development of an interpersonal relationship with one’s pedagogical agent may play a significant role in learning. However, there is conflicting evidence in the literature both regarding the ability of agents to cultivate social relationships with humans, and their effect on learning. In this dissertation, I present a model of social dialog designed to affect learners’ interpersonal relations with virtual agents, a development process for creating social dialog, and empirical studies showing that this dialog has significant effects on learners’ perceptions of the agents and negotiation performance. In early work, I explicitly prompted learners to have social goals for the interaction. I found that while students who reported social goals for interacting with the agents had significantly higher learning gains, explicit prompting was not effective at inducing these goals. I thus focused on implicit influence of learner goals, developing a model of social instructional dialog (SID) that integrates conversational strategies that are theorized to produce interpersonal effects on relationships. In two subsequent studies, an agent with the SID model engendered greater feelings of entitativity, shared perspective, and trust, suggesting that the model improved learner social relationships with the agent. Importantly, these effects transferred to other agents encountered later in the environment. The social dialog condition also made fewer errors and achieved more negotiation objectives in a subsequent negotiation than a control group, evidence that the improved social relationship lead to better negotiation performance. These findings regarding interpersonal relationships with agents contribute to the literature on learner-agent interactions, and can guide the future development of agents in social environments.

Title: Controlling Virtual People Author: Mgr. Jakub Gemrot Department: Department of Software and Computer Science Education Supervisor: Mgr. Cyril Brom, PhD. Abstract: In this thesis, we provide a computational formalization of reactive planning as a paradigm for decision making of intelligent virtual agents and videogame non-player characters. We formalize agent decision-making (ADM) as a process of deciding on which body actions to execute next and differentiate it from agent reasoning as a process of computing facts needed for decision making. We show that imperative programming languages are not suitable for ADM specification and explain why they are not suitable. Thereafter, we create a new computational model that we use as the basis for the definition of Behavior Design Language (BDL). We show that BDL can model decision-making specified by scripting, hierarchical finite-state machines, AgentSpeak(L), GOAL, SPOSH and Behavior trees. Importantly, BDL can model these approaches economically in terms of the number of behavior primitives. The key strengths of the BDL language are: versatility (it can mix decision making patterns of multiple languages together), extensibility (it allows developers to devise new language primitives as they see fit), and generality (it can abstract any computable...

Les émotions et leurs expressions par des agents virtuels sont deux enjeux importants pour les interfaces homme-machine affectives à venir. En effet, les évolutions récentes des travaux en psychologie des émotions, ainsi que la progression des techniques de l’informatique graphique, permettent aujourd’hui d’animer des personnages virtuels réalistes et capables d’exprimer leurs émotions via de nombreuses modalités. Si plusieurs systèmes d’agents virtuels existent, ils restent encore limités par la diversité des modèles d’émotions utilisés, par leur niveau de réalisme, et par leurs capacités d’interaction temps réel. Dans nos recherches, nous nous intéressons aux agents virtuels capables d’exprimer des émotions via leurs expressions faciales en situation d’interaction avec l’utilisateur. Nos travaux posent de nombreuses questions scientifiques et ouvrent sur les problématiques suivantes : Comment modéliser les émotions en informatique en se basant sur les différentes approches des émotions en psychologie ? Quel niveau de réalisme visuel de l’agent est nécessaire pour permettre une bonne expressivité émotionnelle ? Comment permettre l’interaction temps réel avec un agent virtuel ? Comment évaluer l’impact des émotions exprimées par l’agent virtuel sur l’utilisateur ? A partir de ces problématiques, nous avons axé nos travaux sur la modélisation informatique des émotions et sur leurs expressions faciales par un personnage virtuel réaliste. En effet, les expressions faciales sont une modalité privilégiée de la communication émotionnelle. Notre objectif principal est de contribuer l’amélioration de l’interaction entre l’utilisateur et un agent virtuel expressif. Nos études ont donc pour objectif de mettre en lumière les avantages et les inconvénients des différentes approches des émotions ainsi que des méthodes graphiques étudiées. Nous avons travaillé selon deux axes de recherches complémentaires. D’une part, nous avons exploré différentes approches des émotions (catégorielle, dimensionnelle, cognitive, et sociale). Pour chacune de ces approches, nous proposons un modèle informatique et une méthode d’animation faciale temps réel associée. Notre second axe de recherche porte sur l’apport du réalisme visuel et du niveau de détail graphique à l’expressivité de l’agent. Cet axe est complémentaire au premier, car un plus grand niveau de détail visuel pourrait permettre de mieux refléter la complexité du modèle émotionnel informatique utilisé. Les travaux que nous avons effectués selon ces deux axes ont été évalués par des études perceptives menées sur des utilisateurs.La combinaison de ces deux axes de recherche est rare dans les systèmes d’agents virtuels expressifs existants. Ainsi, nos travaux ouvrent des perspectives pour l’amélioration de la conception d’agents virtuels expressifs et de la qualité de l’interaction homme machine basée sur les agents virtuels expressifs interactifs. L’ensemble des logiciels que nous avons conçus forme notre plateforme d’agents virtuels MARC (Multimodal Affective and Reactive Characters). MARC a été utilisée dans des applications de natures diverses : jeu, intelligence ambiante, réalité virtuelle, applications thérapeutiques, performances artistiques, etc. / Emotions and their expressions by virtual characters are two important issues for future affective human-machine interfaces. Recent advances in psychology of emotions as well as recent progress in computer graphics allow us to animate virtual characters that are capable of expressing emotions in a realistic way through various modalities. Existing virtual agent systems are often limited in terms of underlying emotional models, visual realism, and real-time interaction capabilities. In our research, we focus on virtual agents capable of expressing emotions through facial expressions while interacting with the user. Our work raises several issues: How can we design computational models of emotions inspired by the different approaches to emotion in Psychology? What is the level of visual realism required for the agent to express emotions? How can we enable real-time interaction with a virtual agent? How can we evaluate the impact on the user of the emotions expressed by the virtual agent? Our work focuses on computational modeling of emotions inspired by psychological theories of emotion and emotional facial expressions by a realistic virtual character. Facial expressions are known to be a privileged emotional communication modality. Our main goal is to contribute to the improvement of the interaction between a user and an expressive virtual agent. For this purpose, our research highlights the pros and cons of different approaches to emotions and different computer graphics techniques. We worked in two complementary directions. First, we explored different approaches to emotions (categorical, dimensional, cognitive, and social). For each of these approaches, a computational model has been designed together with a method for real-time facial animation. Our second line of research focuses on the contribution of visual realism and the level of graphic detail of the expressiveness of the agent. This axis is complementary to the first one, because a greater level of visual detail could contribute to a better expression of the complexity of the underlying computational model of emotion. Our work along these two lines was evaluated by several user-based perceptual studies. The combination of these two lines of research is seldom in existing expressive virtual agents systems. Our work opens future directions for improving human-computer interaction based on expressive and interactive virtual agents. The software modules that we have designed are integrated into our platform MARC (Multimodal Affective and Reactive Characters). MARC has been used in various kinds of applications: games, ubiquitous intelligence, virtual reality, therapeutic applications, performance art, etc.

In the wake of motion capture and visual animation one aspect seems to be lacking. A realistic representation of the dynamic and unpredictable human visual perception. A human examines her surroundings in a unpredictable, saliency based and top-down task oriented manner. In the field of computer science, interaction design and the industry of game development, great leaps have been taken when it comes to capture motion of bodies. Motion capture helps developers and film makers to portray realistic humans in virtual environments. Where motion has come far, eyes and perception has not. As of yet a virtual representation of human visual perception, has not been mimicked as close as body motion. This thesis will examine perceived realism in virtual agents, with a focus on eye motion. In this study a virtual agent has been given eye movements of human beings and been compared to an agent based on current virtual agents in games. This is the first step towards synthesizing more than just human motion in virtual agents. It will provide future research with the data and tools needed to produce an algorithm based on the gathered data. Prior context research includes a study of current games. Two participant experiments have been be conducted, both has recorded eye positional data for analysis. The first experiment helps build the second as it compares virtual agents using a Likert scale for a subjective rating of realism. The results offers some very interesting data, indeed data that lie at the core of the study as well as data for further studies. While statistical analyses of Likert scales might be considered ambiguous this study has done so and reached a conclusion. A virtual agent enhanced with eye-motion based on human eye movements does portray a more realistic human like behaviour. / Arbetet undersöker uppfattad realism av virtuella karaktärer som använder mänskliga ögonrörelser. Med hjälp av en Tobii Eye-tracker har personers ögonrörelser spelats in medan de tittat på en virtuell karaktär. Därefter har den informationen använts till att skapa en virtuell karaktär med mänskliga ögonrörelser. En jämförelse mellan dessa har sedan gjorts för att bedöma vad som uppfattas mest realistiskt. Resultaten kan inte statistik säkerställas, även om datan ger indikationer på att det är en skillnad. / Programme: Master of Science Programme in Design, Interaction and Game Technologies/Masterprogram i design, interaktion och spelteknologier Phone nr: +46735305836

В работе производиться анализ многоагентных интеллектуальных систем, их различия, способы и направления применения. Описываются программы и методы создания аудио управляемого синтеза лица. Также обсуждаются различные цифровые голосовые помощники, виртуальные агенты. Рассматривается гипотеза и перспективы создания визуального виртуального цифрового помощника для средств массовой информации. / The paper analyzes multi-agent intelligent systems, their differences, ways and directions of application. Programs and methods for creating audio-controlled face synthesis are described. Various digital voice assistants and virtual agents are also discussed. The hypothesis and prospects of creating a visual virtual digital assistant for mass media are considered.

Technological advances will allow virtual agents to increasingly help individuals with daily activities. As such, virtual agents will interact with users of various ages and experience levels. Facial expressions are often used to facilitate social interaction between agents and humans. However, older and younger adults do not label human or virtual agent facial expressions in the same way, with older adults commonly mislabeling certain expressions. The dynamic formation of facial expression, or motion, may provide additional facial information potentially making emotions less ambiguous. This study examined how motion affects younger and older adults in recognizing various intensities of emotion displayed by a virtual agent. Contrary to the dynamic advantage found in emotion recognition for human faces, older adults had higher emotion recognition for static virtual agent faces than dynamic ones. Motion condition did not influence younger adults' emotion recognition. Younger adults had higher emotion recognition than older adults for the emotions of anger, disgust, fear, happiness, and sadness. Low intensities of expression had lower emotion recognition than medium to high expression intensities.