Knowing Ourselves: Building an Interactive Researcher Map at the University of Alberta

Despite claims to interdisciplinarity, universities typically organize knowledge along disciplinary lines in departments and faculties. Institutes like KIAS at the University of Alberta (UofA) have been set up to encourage the development of interdisciplinary research projects, but what do we really know about the research of our colleagues and the connections among them other than their departmental affiliation? How is an institute or interdisciplinary group to know what research directions are pursued by its constituency? Knowledge is vital, and yet Universities struggle to know their own research community This paper describes the development of a research network map of the interests of the humanists, social scientists, and artists at the UofA, which is part of KIAS’ project to understand where there were interdisciplinary strengths at the university and to help connect researchers. In particular, we will describe the challenges around gathering information at an institutional level, and demonstrate the Research Map. The Research Map is a web-based network visualization that shows the connections between faculty members, their research interests, and their departmental affiliation. The outcome is a visualization showing clusters of knowledge and webs of intersectionality, revealing not only the richness of the academic production but also the possibilities of future collaboration between scholars and departments. We are now in the process of adapting the Research Map to be used by others research groups like the Digital Synergies research group. It is a “signature area” for research and creative collaboration focused on digital society, digital methodology, and digital literacies. Adapting the Research Map to be embedded in a website streamlines the process of organizing and visualizing the connections between researchers. We are, in effect, using digital social network analysis methods to help people understand the interdisciplinary network itself

Adventures of AI Directors Early in the Development of Nightingale

Players can sometimes engage with parts of a video game that they do not enjoy if the game does not try to adapt the experience to the player’s preference. AI directors have been used in the past to tailor player experience to different people. In industry, AI directors are relatively uncommon and are typically domain-specific and rules-based. In this paper, we present a reinforcement learning-based AI director developed for the industry game Nightingale with the help of Inflexion Games. We ran an experiment to evaluate the effectiveness of the AI director in creating a desired player experience, but found inconclusive evidence. In line with this year’s theme, we present our negative results and their implications for future AI directors, along with general discussion from working closely with an industry partner