A Generative Model of Group Conversation

Conversations with non-player characters (NPCs) in games are typically confined to dialogue between a human player and a virtual agent, where the conversation is initiated and controlled by the player. To create richer, more believable environments for players, we need conversational behavior to reflect initiative on the part of the NPCs, including conversations that include multiple NPCs who interact with one another as well as the player. We describe a generative computational model of group conversation between agents, an abstract simulation of discussion in a small group setting. We define conversational interactions in terms of rules for turn taking and interruption, as well as belief change, sentiment change, and emotional response, all of which are dependent on agent personality, context, and relationships. We evaluate our model using a parameterized expressive range analysis, observing correlations between simulation parameters and features of the resulting conversations. This analysis confirms, for example, that character personalities will predict how often they speak, and that heterogeneous groups of characters will generate more belief change.Comment: Accepted submission for the Workshop on Non-Player Characters and Social Believability in Games at FDG 201

Fully generated scripted dialogue for embodied agents

This paper presents the NECA approach to the generation of dialogues between Embodied Conversational Agents (ECAs). This approach consist of the automated construction of an abstract script for an entire dialogue (cast in terms of dialogue acts), which is incrementally enhanced by a series of modules and finally ''performed'' by means of text, speech and body language, by a cast of ECAs. The approach makes it possible to automatically produce a large variety of highly expressive dialogues, some of whose essential properties are under the control of a user. The paper discusses the advantages and disadvantages of NECA's approach to Fully Generated Scripted Dialogue (FGSD), and explains the main techniques used in the two demonstrators that were built. The paper can be read as a survey of issues and techniques in the construction of ECAs, focusing on the generation of behaviour (i.e., focusing on information presentation) rather than on interpretation

E-Drama: Facilitating Online Role-play using an AI Actor and Emotionally Expressive Characters.

This paper describes a multi-user role-playing environment, e-drama, which enables groups of people to converse online, in scenario driven virtual environments. The starting point of this research – edrama – is a 2D graphical environment in which users are represented by static cartoon figures. An application has been developed to enable integration of the existing edrama tool with several new components to support avatars with emotionally expressive behaviours, rendered in a 3D environment. The functionality includes the extraction of affect from open-ended improvisational text. The results of the affective analysis are then used to: (a) control an automated improvisational AI actor – EMMA (emotion, metaphor and affect) that operates a bit-part character in the improvisation; (b) drive the animations of avatars using the Demeanour framework in the user interface so that they react bodily in ways that are consistent with the affect that they are expressing. Finally, we describe user trials that demonstrate that the changes made improve the quality of social interaction and users’ sense of presence. Moreover, our system has the potential to evolve normal classroom education for young people with or without learning disabilities by providing 24/7 efficient personalised social skill, language and career training via role-play and offering automatic monitoring

Emotion-driven interactive storytelling.

Interactive storytelling has attracted plenty of research interest in recent years. Most current interactive storytelling systems follow a goal-oriented approach to story representation, i.e. the user is engaged with the story through fulfilling a number of goals rather than empathising with the characters and experiencing anenriched emotional experience (Pizzi and Cavazza 2007). This fails to satisfy potential users who are oriented to traditional media, such as movies (Louchart et al. 2008) and demographic groups who are interested in attractive and challenging stories (Duh et al. 2010). Given this consideration, an emotion-driven interactive storytelling approach is proposed in this research. In contrast to the goal-oriented interactive storytelling approach, emotion-driven interactive storytelling attempts to create an engaging emotional experience, and involve the user’s emotion with the characters. More importantly, the user’s emotions, evoked by empathising with the characters, determine the character’s behaviours and therefore have an impact on the whole storyline. In this sense, emotions, as a driving force, directly and explicitly contribute to storytelling and the user experience. An interactive video was made by re-editing existing TV material to interpret the concept of emotion-driven interactive storytelling. The examination of user experience of playing this interactive video revealed that non-gamers were more likely to be emotionally involved with the interactive video and empathise with the character. Participants in this group also exhibited higher enjoyment and engagement than gamers. In addition, females were found more likely to empathise with the character and satisfy with the storyline. However because the TV material used to make the interactive video was female-oriented, males failed to enjoy and engage themselves as much as females. But it is important to note that in comparison to males’ previous experience of watching TV Ugly Betty, emotion-driven interactive storytelling increased their enjoyment and engagement. Therefore, emotion-driven interactive storytelling enriches the approach to developing interactive storytelling systems and has the potential to provide an engaging user experience to some types of users. Future research possibilities are discussed with respect to a wider population and research where materials suitable for both genders are presented

Machinima And Video-based Soft Skills Training

Multimedia training methods have traditionally relied heavily on video based technologies and significant research has shown these to be very effective training tools. However production of video is time and resource intensive. Machinima (pronounced \u27muh-sheen-eh-mah\u27) technologies are based on video gaming technology. Machinima technology allows video game technology to be manipulated into unique scenarios based on entertainment or training and practice applications. Machinima is the converting of these unique scenarios into video vignettes that tell a story. These vignettes can be interconnected with branching points in much the same way that education videos are interconnected as vignettes between decision points. This study addressed the effectiveness of machinima based soft-skills education using avatar actors versus the traditional video teaching application using human actors. This research also investigated the difference between presence reactions when using avatar actor produced video vignettes as compared to human actor produced video vignettes. Results indicated that the difference in training and/or practice effectiveness is statistically insignificant for presence, interactivity, quality and the skill of assertiveness. The skill of active listening presented a mixed result indicating the need for careful attention to detail in situations where body language and facial expressions are critical to communication. This study demonstrates that a significant opportunity exists for the exploitation of avatar actors in video based instruction

Fictional Worlds, Real Connections: Developing Community Storytelling Social Chatbots through LLMs

We address the integration of storytelling and Large Language Models (LLMs) to develop engaging and believable Social Chatbots (SCs) in community settings. Motivated by the potential of fictional characters to enhance social interactions, we introduce Storytelling Social Chatbots (SSCs) and the concept of story engineering to transform fictional game characters into "live" social entities within player communities. Our story engineering process includes three steps: (1) Character and story creation, defining the SC's personality and worldview, (2) Presenting Live Stories to the Community, allowing the SC to recount challenges and seek suggestions, and (3) Communication with community members, enabling interaction between the SC and users. We employed the LLM GPT-3 to drive our SSC prototypes, "David" and "Catherine," and evaluated their performance in an online gaming community, "DE (Alias)," on Discord. Our mixed-method analysis, based on questionnaires (N=15) and interviews (N=8) with community members, reveals that storytelling significantly enhances the engagement and believability of SCs in community settings

Artificial and Computational Intelligence in Games (Dagstuhl Seminar 12191)

This report documents the program and the outcomes of Dagstuhl Seminar 12191 "Artificial and Computational Intelligence in Games". The aim for the seminar was to bring together creative experts in an intensive meeting with the common goals of gaining a deeper understanding of various aspects of artificial and computational intelligence in games, to help identify the main challenges in game AI research and the most promising venues to deal with them. This was accomplished mainly by means of workgroups on 14 different topics (ranging from search, learning, and modeling to architectures, narratives, and evaluation), and plenary discussions on the results of the workgroups. This report presents the conclusions that each of the workgroups reached. We also added short descriptions of the few talks that were unrelated to any of the workgroups