Tabletop Role Playing Games (TRPG) are games that require players to become the characters they play through engaging in role-play. The challenge of training AI lies in the requirement of it not only understanding the explicitly stated game rules, but also the implicit ones that come with role-playing. Previous studies endeavouring said challenge allude to aspects of role-play, but do not emphasise its role in their methods, indicating that the definition of role-play and how it is to be employed remains unclear. This short paper aims to investigate a proposed definition of role-play based on previous research and employ its use on Large Language Model LLM, eliciting an understanding thereof through a novel prompting method dubbed Controlled Chain of Thought (CCoT). CCoT allows for the LLM to highlight absence of information in inputs given to it by generating questions, which become the template for its chain of thoughts when answered. This paper presents an initial pilot testing of CCoT as well as opens up the discussion of how a definition of role-play can be beneficial for future studies.

Abstract Games as a service is similar to software as a service, which provides players with game content on a continuous monetization model. Game revenue forecast is vital to game developers to make the right business decisions, such as determining the marketing budget, controlling the development cost, and setting up benchmarks for evaluating game publishing performance. How to make the revenue forecast and integrate it with the game publishing process is hard for small and medium-sized independent (indie) game developers. This includes all steps of the process, from forecasting to decision-making based on the results. This paper provides a data-driven method that uses the mobile game revenue forecast based on different time-series prediction models to drive the game publishing. We demonstrate how to use the data-driven method to guide an indie game studio to forecast revenue and then set the revenue forecast as the internal benchmark to drive game publishing. In practice, we involve a real game project from an indie game studio and provide guidance for one of their casual game projects. Then, based on the revenue forecast, we discuss how to set the revenue forecast as an internal benchmark and drive the actions for mobile game publishing. Finally, we make a conclusion on how our data-driven method can be used to drive mobile game publishing and also discuss future research work.

Abstract As a business model, the essence of games is to provide a service to satisfy the player experience. From a business perspective, development in the game industry has led to the application of Business Intelligence (BI) becoming more and more extensive. However, related research lacks systematic examination and precise classification. This paper provides a comprehensive literature review of BI used in the game industry, focusing primarily on game analytics. This research mainly studies and discusses five aspects. First, we explore game analytics aspects in the available literature based on the traditional game value chain. Second, we find out the main purposes of using analytics in the game industry. Third, we present the problems or challenges in the game area, which can be addressed by using game analytics. Fourth, we also list different algorithms that have been used in game analytics for prediction. Finally, we summarize the research areas that have already been covered in literature but need further development. Based on the categories established after the mapping and the review findings, we also discuss the limitations of game analytics and propose potential research points for future research.

This paper presents and evaluates a new educational tool and a model for learning theory and practice of game development, by involving geographically separated and culturally different industrial and academic organizations. We propose a TRIAD education model, which is as an extension to the conventional project-based learning, by uniting industry staff, faculty members, and students in an online discussion space. This model is a two-phase cyclic model where in Phase-1) industry members and academic staff discuss the progress of PBL transparently, showing the cross-cultural gaps and advising how to solve them, and in Phase-2) students make progress on the projects. Additionally, the TRIAD model adopts asynchronous text messaging as a core communication method, which is suitable for a multi-timezone situation, as well as exchanges of self-reviews from students and feedback from instructors. The online space not only facilitates interns in overcoming the hurdles represented by the gaps between the industry and academia, but it also visualizes the students' implicit educational progress by periodically analyzing all stored data by applying quantitative text analysis methods. This paper shows the result of an empirical study by utilizing this TRIAD model and analytics tool conducted at a large-scale Japanese game company with over 2,000 employees focusing on two students from the division of game development at a university in Sweden. We applied a quantitative text analysis tool for this model during an on-going internship program to clarify how students change their thoughts and behaviors by acquiring cross-cultural professionalism in the game industry. In addition, the result indicates that this TRIAD model is resilient, even under the COVID-19 epidemic situation due to its text-based approach.

This paper presents Sidh–a game based firefighter training simulator developed in cooperation between the University of Skövde and the Swedish Rescue Services Agency. Sidh is based on computer game hardware and software solutions but adds a novel interaction model and gameplay specifically developed for the purpose of training firefighters. The simulator environment is a Cave where the player is interacting with the game through a set of sensors. Players move in the virtual world by movements in the physical world and a substantial physical effort is required to accomplish game tasks. Sidh has been used in a feasibility study where 31 firefighter students have been playing the game and the performance of these students as well as their reflections from using the game have been analyzed. Results from this study show that Sidh is a useful complement to traditional training methods and that the subjects give very high grades on the entertainment value of the game which indicate that this form of training may be self-motivating which is an important issue for voluntary, after-hours training.

In this paper we report results from an experimental study of games and traffic safety performed in an advanced gaming environment. During car simulator-sessions we collected data over different traffic safety variables, such as speed, headway distance and lane change behavior, from 70 subjects. The data was analyzed in order to investigate possible individual learning effects and differences between groupings of subjects. The experiment shows clear positive individual learning effects for all traffic safety variables analyzed. Hence we conclude that game based simulations can be used to enhance learning in driving education.

This work addresses data warehouse maintenance, i.e. how changes to autonomous, heterogeneous, and distributed sources should be detected and propagated to a warehouse. The research community has mainly addressed issues relating to the internal operation of data warehouse servers. Work related to data warehouse maintenance has received less attention and only a limited set of maintenance alternatives are considered while ignoring the autonomy and heterogeneity of sources.In this paper, we extend work on single source view maintenance to views with multiple heterogeneous sources. We present a tool (PAM) which allows for comparison of a large number of relevant maintenance policies under different configurations. Based on such analysis and previous studies we propose a set of heuristics to guide in policy selection. The quality of these heuristics is evaluated empirically using a test-bed developed for this purpose. This is done for a number of different criteria and for different data sources and computer systems. The performance gained using the policy selected through the heuristics is compared with the performance of all identified policies. Based on these experiments we claim that heuristic-based selections are good.

We address data warehouse maintenance, i.e. how changes to autonomous sources should be detected and propagated to a warehouse. We have extended our work on source characteristics and timings relevant to single source views by exploring data integration from (multiple) heterogeneous sources. We identify relevant maintenance policies and develop a set of heuristics to guide policy choice. On the basis of empirical (testbed) experiments, we claim that resulting selections are good.