184 research outputs found

    Structuring AI Teammate Communication: An Exploration of AI\u27s Communication Strategies in Human-AI Teams

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    In the past decades, artificial intelligence (AI) has been implemented in various domains to facilitate humans in their work, such as healthcare and the automotive industry. Such application of AI has led to increasing attention on human-AI teaming, where AI closely collaborates with humans as a teammate. AI as a teammate is expected to have the ability to coordinate with humans by sharing task-related information, predicting other teammates’ behaviors, and progressing team tasks accordingly. To complete these team activities effectively, AI teammates must communicate with humans, such as sharing updates and checking team progress. Even though communication is a core element of teamwork that helps to achieve effective coordination, how to design and structure human-AI communication in teaming environments still remains unclear. Given the context-dependent characteristics of communication, research on human-AI teaming communication needs to narrow down and focus on specific communication elements/components, such as the proactivity of communication and communication content. In doing so, this dissertation explores how AI teammates’ communication should be structured by modifying communication components through three studies, each of which details a critical component of effective AI communication: (1) communication proactivity, (2) communication content (explanation), and (3) communication approach (verbal vs. non-verbal). These studies provide insights into how AI teammates’ communication ii can be integrated into teamwork and how to design AI teammate communication in human-AI teaming. Study 1 explores an important communication element, communication proactivity, and its impact on team processes and team performance. Specifically, communication proactivity in this dissertation refers to whether an AI teammate proactively communicates with human teammates, i.e., proactively pushing information to human teammates. Experimental analysis shows that AI teammates’ proactive communication plays a crucial role in impacting human perceptions, such as perceived teammate performance and satisfaction with the teammate. Importantly, teams with a non-proactive communication AI teammate increase team performance more than teams with a proactive communication AI as the human and the AI collaborate more. This study identifies the positive impact of AI being proactive in communication at the initial stage of task coordination, as well as the potential need for AI’s flexibility in their communication proactivity (i.e., once human and AI teammates’ coordination pattern forms, AI can be non-proactive in communication). Study 2 examines communication content by focusing on AI’s explanation and its impact on human perceptions in teaming environments. Results indicate that AI’s explanation, as part of communication content, does not always positively impact human trust in human-AI teaming. Instead, the impact of AI’s explanations on human perceptions depends on specific collaboration scenarios. Specifically, AI’s explanations facilitate trust in the AI teammate when explaining why AI disobeys humans’ orders, but hinder trust when explaining why AI lies to humans. In addition, AI giving an explanation of why they ignored the human teammate’s injury was perceived to be more effective than AI not providing such an explanation. The findings emphasize the context-dependent characteristic of AI’s communication content with a focus on AI’s explanation of their actions. iii Study 3 investigates AI’s communication approach, which was manipulated as verbal vs. non-verbal communication. Results indicate that AI teammates’ verbal/nonverbal communication does not impact human trust in the AI teammate, but facilitates the maintenance of humans’ situation awareness in task coordination. In addition, AI with non-verbal communication is perceived as having lower communication quality and lower performance. Importantly, AI with non-verbal communication has better team performance in human-human-AI teams than human-AI-AI teams, whereas AI with verbal communication has better team performance in human-AI-AI teams than human-human-AI teams. These three studies together address multiple research gaps in human-AI team communication and provide a holistic view of the design and structure of AI’s communication by examining three specific aspects of communication in human-AI teaming. In addition, each study in this dissertation proposes practical design implications on AI’s communication in human-AI teams, which will assist AI designers and developers to create better AI teammates that facilitate humans in teaming environments

    Fighting \u27Stance\u27: The Role of Conversational Positioning in League of Legends (Multiplayer Online Battle Arena) Discourse

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    For researchers, the study of video game players - how they behave, interact, and cooperate in a virtual world – presents a challenge: what methodologies are best suited to approaching these interactions? From a sociolinguistic approach, how do gamers converse, and what do these conversations reveal about epistemic, affective, and political relationships? This study uses John DuBois’ Stance Theory (2007) and recent modifications of it (Kiesling 2022), to analyze data gathered from the popular multiplayer online battle-arena (MOBA) game League of Legends. It focuses on in-game interlocutors’ conversation samples to show their positioning, intersubjective alignment, and evaluation of a constantly changing speech environment. DuBois’ Stance Triangle permits visualization of the stances taken within such chat-room interactions that focus on player comments concerning the game, game-playing, and other gamers (as well as themselves). In the search for stance identity, DuBois’ model specifically seeks to understand the alignment between interlocutors, the evaluation each interlocutor makes of the stance object, and the position each interlocutor takes with regard to that object. This study builds on the work of researchers in stance-based analysis of gaming discourse (Sierra 2016), multimodality (Collister 2012), and language acquisition (Bakos 2018). This triangulation model will be supplemented with other discourse and pragmatic analyses when necessary, to interpret the stance-taking in a rapidly changing online environment filled with stances often likely to be related to ethical positions and displays of commentary on a range of topics, including the meta-game skills and abilities of the players, and extra-game references, and the intersection of these concepts in the construction of attitudinal positioning, stancetaking, and inter-personal dynamics in a common goal-motivated speech environment

    Player-AI Interaction: What Neural Network Games Reveal About AI as Play

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    The advent of artificial intelligence (AI) and machine learning (ML) bring human-AI interaction to the forefront of HCI research. This paper argues that games are an ideal domain for studying and experimenting with how humans interact with AI. Through a systematic survey of neural network games (n = 38), we identified the dominant interaction metaphors and AI interaction patterns in these games. In addition, we applied existing human-AI interaction guidelines to further shed light on player-AI interaction in the context of AI-infused systems. Our core finding is that AI as play can expand current notions of human-AI interaction, which are predominantly productivity-based. In particular, our work suggests that game and UX designers should consider flow to structure the learning curve of human-AI interaction, incorporate discovery-based learning to play around with the AI and observe the consequences, and offer users an invitation to play to explore new forms of human-AI interaction

    Proceedings of the SAB'06 Workshop on Adaptive Approaches for Optimizing Player Satisfaction in Computer and Physical Games

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    These proceedings contain the papers presented at the Workshop on Adaptive approaches for Optimizing Player Satisfaction in Computer and Physical Games held at the Ninth international conference on the Simulation of Adaptive Behavior (SAB’06): From Animals to Animats 9 in Rome, Italy on 1 October 2006. We were motivated by the current state-of-the-art in intelligent game design using adaptive approaches. Artificial Intelligence (AI) techniques are mainly focused on generating human-like and intelligent character behaviors. Meanwhile there is generally little further analysis of whether these behaviors contribute to the satisfaction of the player. The implicit hypothesis motivating this research is that intelligent opponent behaviors enable the player to gain more satisfaction from the game. This hypothesis may well be true; however, since no notion of entertainment or enjoyment is explicitly defined, there is therefore little evidence that a specific character behavior generates enjoyable games. Our objective for holding this workshop was to encourage the study, development, integration, and evaluation of adaptive methodologies based on richer forms of humanmachine interaction for augmenting gameplay experiences for the player. We wanted to encourage a dialogue among researchers in AI, human-computer interaction and psychology disciplines who investigate dissimilar methodologies for improving gameplay experiences. We expected that this workshop would yield an understanding of state-ofthe- art approaches for capturing and augmenting player satisfaction in interactive systems such as computer games. Our invited speaker was Hakon Steinø, Technical Producer of IO-Interactive, who discussed applied AI research at IO-Interactive, portrayed the future trends of AI in computer game industry and debated the use of academic-oriented methodologies for augmenting player satisfaction. The sessions of presentations and discussions where classified into three themes: Adaptive Learning, Examples of Adaptive Games and Player Modeling. The Workshop Committee did a great job in providing suggestions and informative reviews for the submissions; thank you! This workshop was in part supported by the Danish National Research Council (project no: 274-05-0511). Finally, thanks to all the participants; we hope you found this to be useful!peer-reviewe

    The promotion of ethical egoism through morality mechanics in mass effect, fable III & fallout new vegas: a role-playing video game exploration

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    The aim of this study is to determine whether or not ethical egoism is promoted during gameplay of three role-playing video games namely Mass Effect, Fable III and Fallout New Vegas. The rapid expansion of the video gaming industry as well as game studies as an academic field have made it necessary to understand what effects video games may have on society. This study shows that gamers come into contact with various ethico-moral dilemmas during gameplay and act in an egoistic manner in order to complete video games. Firstly, an explanation of game and gameplay are provided as well as an outline of two game studies methodologies, namely narratology and ludology. These two methodologies are then combined into a hybrid approach which is used to analyse the video games from both a narrative and gameplay point of view which allows for a more comprehensive analysis of each respective game. Thereafter, a discussion of B.F. Skinner's behaviourism is given in order to better understand gamer behaviour. Skinner's concepts of positive reinforcement, schedules of reinforcement and operant conditioning are then linked to video games to show behaviourism's influences on game design. Ethical egoism, as theorised by Thomas Hobbes and Jesse Kalin, provides the ethico-moral theory necessary for the analysis of the morality mechanic in each game. Ethico-moral dilemmas identified within each game are discussed with regards to the hybrid approach which details both narrative and gameplay consequences of in-game ethico-moral decision making. The study concludes that gamers are ethical egoists when engaged in gameplay, due to their desire to complete the video game. However, during gameplay, gamers are exposed to altruism which is often promoted through the narrative and the nature of in-game objectives. Suggestions for further studies are also given for example a more detailed analysis of gamer behaviour, a qualitative study of in-game ethico-moral actions as well as a study of games that are non-violent in nature

    Facial and Bodily Expressions for Control and Adaptation of Games (ECAG 2008)

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