Mobile exergaming in adolescents’ everyday life—contextual design of where, when, with whom, and how: the SmartLife case

Exergames, more specifically console-based exergames, are generally enjoyed by adolescents and known to increase physical activity. Nevertheless, they have a reduced usage over time and demonstrate little effectiveness over the long term. In order to increase playing time, mobile exergames may increase potential playing time, but need to be engaging and integrated in everyday life. The goal of the present study was to examine the context of gameplay for mobile exergaming in adolescents’ everyday life to inform game design and the integration of gameplay into everyday life. Eight focus groups were conducted with 49 Flemish adolescents (11 to 17 years of age). The focus groups were audiotaped, transcribed, and analyzed by means of thematic analysis via Nvivo 11 software (QSR International Pty Ltd., Victoria, Australia). The adolescents indicated leisure time and travel time to and from school as suitable timeframes for playing a mobile exergame. Outdoor gameplay should be restricted to the personal living environment of adolescents. Besides outdoor locations, the game should also be adaptable to at-home activities. Activities could vary from running outside to fitness exercises inside. Furthermore, the social context of the game was important, e.g., playing in teams or meeting at (virtual) meeting points. Physical activity tracking via smart clothing was identified as a motivator for gameplay. By means of this study, game developers may be better equipped to develop mobile exergames that embed gameplay in adolescents’ everyday life

Language Model-In-The-Loop: Data Optimal Approach to Learn-To-Recommend Actions in Text Games

Large Language Models (LLMs) have demonstrated superior performance in language understanding benchmarks. CALM, a popular approach, leverages linguistic priors of LLMs -- GPT-2 -- for action candidate recommendations to improve the performance in text games in Jericho without environment-provided actions. However, CALM adapts GPT-2 with annotated human gameplays and keeps the LLM fixed during the learning of the text based games. In this work, we explore and evaluate updating LLM used for candidate recommendation during the learning of the text based game as well to mitigate the reliance on the human annotated gameplays, which are costly to acquire. We observe that by updating the LLM during learning using carefully selected in-game transitions, we can reduce the dependency on using human annotated game plays for fine-tuning the LLMs. We conducted further analysis to study the transferability of the updated LLMs and observed that transferring in-game trained models to other games did not result in a consistent transfer

Open Player Modeling: Empowering Players through Data Transparency

Data is becoming an important central point for making design decisions for most software. Game development is not an exception. As data-driven methods and systems start to populate these environments, a good question is: can we make models developed from this data transparent to users? In this paper, we synthesize existing work from the Intelligent User Interface and Learning Science research communities, where they started to investigate the potential of making such data and models available to users. We then present a new area exploring this question, which we call Open Player Modeling, as an emerging research area. We define the design space of Open Player Models and present exciting open problems that the games research community can explore. We conclude the paper with a case study and discuss the potential value of this approach

LLM-Based Agent Society Investigation: Collaboration and Confrontation in Avalon Gameplay

This paper aims to investigate the open research problem of uncovering the social behaviors of LLM-based agents. To achieve this goal, we adopt Avalon, a representative communication game, as the environment and use system prompts to guide LLM agents to play the game. While previous studies have conducted preliminary investigations into gameplay with LLM agents, there lacks research on their social behaviors. In this paper, we present a novel framework designed to seamlessly adapt to Avalon gameplay. The core of our proposed framework is a multi-agent system that enables efficient communication and interaction among agents. We evaluate the performance of our framework based on metrics from two perspectives: winning the game and analyzing the social behaviors of LLM agents. Our results demonstrate the effectiveness of our framework in generating adaptive and intelligent agents and highlight the potential of LLM-based agents in addressing the challenges associated with dynamic social environment interaction. By analyzing the social behaviors of LLM agents from the aspects of both collaboration and confrontation, we provide insights into the research and applications of this domain

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