From individual characters to large crowds: augmenting the believability of open-world games through exploring social emotion in pedestrian groups

Crowds of non-player characters improve the game-play experiences of open-world video-games. Grouping is a common phenomenon of crowds and plays an important role in crowd behaviour. Recent crowd simulation research focuses on group modelling in pedestrian crowds and game-designers have argued that the design of non-player characters should capture and exploit the relationship between characters. The concepts of social groups and inter-character relationships are not new in social psychology, and on-going work addresses the social life of emotions and its behavioural consequences on individuals and groups alike. The aim of this paper is to provide an overview of current research in social psychology, and to use the findings as a source of inspiration to design a social network of non-player characters, with application to the problem of group modelling in simulated crowds in computer games

NPCs as People, Too: The Extreme AI Personality Engine

PK Dick once asked “Do Androids Dream of Electric Sheep?” In video games, a similar question could be asked of non-player characters: Do NPCs have dreams? Can they live and change as humans do? Can NPCs have personalities, and can these develop through interactions with players, other NPCs, and the world around them? Despite advances in personality AI for games, most NPCs are still undeveloped and undeveloping, reacting with flat affect and predictable routines that make them far less than human— in fact, they become little more than bits of the scenery that give out parcels of information. This need not be the case. Extreme AI, a psychology-based personality engine, creates adaptive NPC personalities. Originally developed as part of the thesis “NPCs as People: Using Databases and Behaviour Trees to Give Non-Player Characters Personality,” Extreme AI is now a fully functioning personality engine using all thirty facets of the Five Factor model of personality and an AI system that is live throughout gameplay. This paper discusses the research leading to Extreme AI; develops the ideas found in that thesis; discusses the development of other personality engines; and provides examples of Extreme AI’s use in two game demos

챗봇이 신뢰 위반으로부터 회복하는 데 사과와 공감이 미치는 영향

학위논문(석사) -- 서울대학교대학원 : 사회과학대학 심리학과, 2022. 8. 한소원.In the present study, we investigated how chatbots can recover user trust after making errors. In two experiments, participants had a conversation with a chatbot about their daily lives and personal goals. After giving an inadequate response to the user’s negative sentiments, the chatbot apologized using internal or external error attribution and various levels of empathy. Study 1 showed that the type of apology did not affect users’ trust or the chatbot’s perceived competence, warmth, or discomfort. Study 2 showed that short apologies increased trust and perceived competence of the chatbot compared to long apologies. In addition, apologies with internal attribution increased the perceived competence of the chatbot. The perceived comfort of the chatbot increased when apologies with internal attribution were longer as well as when apologies with external attribution were shorter. However, in both Study 1 and Study 2, the apology conditions did not significantly increase users’ trust or positively affect their perception of the chatbot in comparison to the no-apology condition. Our research provides practical guidelines for designing error recovery strategies for chatbots. The findings demonstrate that Human-Robot Interaction may require an approach to trust recovery that differs from Human-Human Interaction.본 연구에서는 챗봇이 대화 중 오류가 있었을 때 사용자의 신뢰를 회복할 수 있는 방법에 대하여 탐색하였다. 두 번의 실험에서 참여자들은 일상생활과 자신의 목표에 관하여 챗봇과 대화를 나누었다. 챗봇은 참여자의 부정적 감정에 대해 부적절한 응답을 한 후, 공감 수준을 달리하며 내적 귀인 혹은 외적 귀인을 사용하여 사과했다. 연구 1에 따르면 사과의 종류는 사용자의 신뢰나 챗봇의 지각된 유능함, 따뜻함, 불편감에 유의미한 영향을 주지 않았다. 연구 2 결과 짧은 사과는 긴 사과보다 챗봇에 대한 사용자의 신뢰와 지각된 유능함을 더 크게 높였다. 또한, 내적 귀인을 사용하는 사과가 챗봇의 지각된 유능함을 더 크게 향상시켰다. 내적 귀인을 사용하는 사과의 경우 길이가 길 때, 외적 귀인을 사용하는 사과의 경우 길이가 짧을 때 사용자들에게 더 편안하게 느껴졌다. 그러나 연구 1과 연구 2 모두에서 사과 조건은 사용자의 신뢰를 유의미하게 증가시키거나 챗봇의 인식에 유의미하게 긍정적인 영향을 미치지 않았다. 본 연구는 챗봇 오류를 해결하기 위한 신뢰 회복 전략을 수립하기 위한 실용적인 지침을 제공한다. 또한, 본 연구 결과는 인간-로봇 상호작용에서 요구되는 신뢰 회복 전략은 인간-인간 상호 작용에서 사용되는 전략과는 상이할 수 있음을 보여준다.Abstract i Table of Contents ii List of Tables iii List of Figures iii Chapter 1. Introduction 1 1. Motivation 1 2. Previous Research 2 3. Purpose of Study 11 Chapter 2. Study 1 12 1. Hypotheses 12 2. Methods 12 3. Results 18 4. Discussion 23 Chapter 3. Study 2 25 1. Hypotheses 25 2. Methods 26 3. Results 30 4. Discussion 38 Chapter 4. Conclusion 40 Chapter 5. General Discussion 42 References 46 Appendix 54 국문초록 65석

Artificial Companions with Personality and Social Role

Subtitle: "Expectations from Users on the Design of Groups of Companions"International audienceRobots and virtual characters are becoming increasingly used in our everyday life. Yet, they are still far from being able to maintain long-term social relationships with users. It also remains unclear what future users will expect from these so-called "artificial companions" in terms of social roles and personality. These questions are of importance because users will be surrounded with multiple artificial companions. These issues of social roles and personality among a group of companions are sledom tackled in user studies. In this paper, we describe a study in which 94 participants reported that social roles and personalities they would expect from groups of companions. We explain how the resulsts give insights for the design of future groups of companions endowed with social intelligence

Creepy, but Persuasive: In a Virtual Consultation, Physician Bedside Manner, Rather than the Uncanny Valley, Predicts Adherence

Care for chronic disease requires patient adherence to treatment advice. Nonadherence worsens health outcomes and increases healthcare costs. When healthcare professionals are in short supply, a virtual physician could serve as a persuasive technology to promote adherence. However, acceptance of advice may be hampered by the uncanny valley effect—a feeling of eeriness elicited by human simulations. In a hypothetical virtual doctor consultation, 441 participants assumed the patient’s role. Variables from the stereotype content model and the heuristic–systematic model were used to predict adherence intention and behavior change. This 2 × 5 between-groups experiment manipulated the doctor’s bedside manner—either good or poor—and virtual depiction at five levels of realism. These independent variables were designed to manipulate the doctor’s level of warmth and eeriness. In hypothesis testing, depiction had a nonsignificant effect on adherence intention and diet and exercise change, even though the 3-D computer-animated versions of the doctor (i.e., animation, swapped, and bigeye) were perceived as eerier than the others (i.e., real and cartoon). The low-warmth, high-eeriness doctor prompted heuristic processing of information, while the high-warmth doctor prompted systematic processing. This pattern contradicts evidence reported in the persuasion literature. For the stereotype content model, a path analysis found that good bedside manner increased the doctor’s perceived warmth significantly, which indirectly increased physical activity. For the heuristic–systematic model, the doctor’s eeriness, measured in a pretest, had no significant effect on adherence intention and physical activity, while good bedside manner increased both significantly. Surprisingly, cognitive perspective-taking was a stronger predictor of change in physical activity than adherence intention. Although virtual characters can elicit the uncanny valley effect, their effect on adherence intention and physical activity was comparable to a video of a real person. This finding supports the development of virtual consultations

The doctor’s digital double: how warmth, competence, and animation promote adherence intention

Background Each year, patient nonadherence to treatment advice costs the US healthcare system more than $300 billion and results in 250,000 deaths. Developing virtual consultations to promote adherence could improve public health while cutting healthcare costs and usage. However, inconsistencies in the realism of computer-animated humans may cause them to appear eerie, a phenomenon termed the uncanny valley. Eeriness could reduce a virtual doctor’s credibility and patients’ adherence. Methods In a 2 × 2 × 2 between-groups posttest-only experiment, 738 participants played the role of a patient in a hypothetical virtual consultation with a doctor. The consultation varied in the doctor’s Character (good or poor bedside manner), Outcome (received a fellowship or sued for malpractice), and Depiction (a recorded video of a real human actor or of his 3D computer-animated double). Character, Outcome, and Depiction were designed to manipulate the doctor’s level of warmth, competence, and realism, respectively. Results Warmth and competence increased adherence intention and consultation enjoyment, but realism did not. On the contrary, the computer-animated doctor increased adherence intention and consultation enjoyment significantly more than the doctor portrayed by a human actor. We propose that enjoyment of the animated consultation caused the doctor to appear warmer and more real, compensating for his realism inconsistency. Expressed as a path model, this explanation fit the data. Discussion The acceptance and effectiveness of the animation should encourage the development of virtual consultations, which have advantages over creating content with human actors including ease of scenario revision, internationalization, localization, personalization, and web distribution

Managing an agent's self-presentational strategies during an interaction

In this paper we present a computational model for managing the impressions of warmth and competence (the two fundamental dimensions of social cognition) of an Embodied Conversational Agent (ECA) while interacting with a human. The ECA can choose among four different self-presentational strategies eliciting different impressions of warmth and/or competence in the user, through its verbal and non-verbal behavior. The choice of the non-verbal behaviors displayed by the ECA relies on our previous studies. In our first study, we annotated videos of human-human natural interactions of an expert on a given topic talking to a novice, in order to find associations between the warmth and competence elicited by the expert's non-verbal behaviors (such as type of gestures, arms rest poses, smiling). In a second study, we investigated whether the most relevant non-verbal cues found in the previous study were perceived in the same way when displayed by an ECA. The computational learning model presented in this paper aims to learn in real-time the best strategy (i.e., the degree of warmth and/or competence to display) for the ECA, that is, the one which maximizes user's engagement during the interaction. We also present an evaluation study, aiming to investigate our model in a real context. In the experimental scenario, the ECA plays the role of a museum guide introducing an exposition about video games. We collected data from 75 visitors of a science museum. The ECA was displayed in human dimension on a big screen in front of the participant, with a Kinect on the top. During the interaction, the ECA could adopt one of 4 self-presentational strategies during the whole interaction, or it could select one strategy randomly for each speaking turn, or it could use a reinforcement learning algorithm to choose the strategy having the highest reward (i.e., user's engagement) after each speaking turn

Gender stereotypes in virtual agents

Visual, behavioural and verbal cues for gender are often used in designing virtual agents to take advantage of their cultural and stereotypical effects on the users. However, recent studies point towards a more gender-balanced view of stereotypical traits and roles in our society. This thesis is intended as an effort towards a progressive and inclusive approach for gender representations in virtual agents. The contributions are two-fold. First, in an iterative design process, representative male, female and androgynous embodied AI agents were created with few differences in their visual attributes. Second, these agents were then used to evaluate the stereotypical assumptions of gendered traits and roles in AI virtual agents. The results showed that, indeed, gender stereotypes are not as effective as previously assumed, and androgynous agents could represent a middle-ground between gendered stereotypes. The thesis findings are presented in the hope to foster discussions in virtual agent research and the frequent stereotypical use of gender representations

Trust in interdependent and task-oriented human-computer cooperation

Kulms P. Trust in interdependent and task-oriented human-computer cooperation. Bielefeld: Universität Bielefeld; 2018.This thesis presents a new paradigm for the modeling of cooperative human–computer interaction in order to evaluate the antecedents, formation, and regulation of human–computer trust. Human–computer trust is the degree to which human users trust computers to help them achieve their goals, and functions as powerful psychological variable that governs user behavior. The modeling framework presented in this thesis aims to extend predominant methods for the study of trust and cooperation by building on competent problemsolving and equal goal contributions by users and computers. Specifically, the framework permits users to participate in interactive and interdependent decision-making games with autonomous computer agents. The main task is to solve a two-dimensional puzzle, similar to the popular game Tetris. The games derived from this framework include cooperative interaction factors known from interpersonal cooperation: the duality of competence and selfishness, anthropomorphism, task advice, and social blame. One validation study (68 participants) and four experiments (318 participants) investigate how these cooperative interaction factors influence human–computer trust. In particular, the results show how trust in computers is mediated by warmth as universal dimension of social cognition, how anthropomorphism of computers influences trust formation over time, and how expressive anthropomorphic cues can be used to regulate trust. We explain how these findings can be applied to design trustworthy computer agents for successful cooperation