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Geogames - Intention Recognition and Data Quality in Location-based Gaming

Mobility is often regarded as a necessity we cannot avoid due to professional and private activities in our daily lives. Waiting for the train, being trapped in a traffic jam, and the like, are generally perceived as time-consuming and enervating. Mobile computing has opened a bunch of new possibilities for entertainment which can help us to make the best of our journey. Mobile phone games and mobile TV (e.g. [9]) are typical examples. Most of these systems try to port a stationary entertainment experience to a mobile device. The spatio-temporal movement is seen as an unavoidable precondition for which we must find a technical solution, but not as an entertaining element itself. Exactly this issue is addressed by location-based games (LBG), which take use of positioning technology (e.g. GPS) and integrate the player’s position into the logics of a game [11]. This approach of not only creating entertainment for mobility, but also creating entertainment from mobility seems quite promising. Projects in the pervasive gaming community have proposed a number of LBGs [3], and addressed various research questions. However, the AI aspects connected with LBGs have largely been ignored. The Geogames project aims at exploring how methods from AI can be used in all phases of a LBG: before, during, and after the game. The research directions addressed in the project follow these three phases. We are especially interested in the implications of space and time on AI in LBGs. The following questions guide our research: 1. Game design and setup: how can we create a game that addresses both, the player’s intellectual and sportive skills? How does the choice of geographic footprints influence the spatio-temporal flow of a game? And, closely related: how can we help the game designer to port a game to a new geographic area? (section 2) 2. During the game: how can we infer the player’s intentions to act from her spatio-temporal behavior (mobile intention recognition)? How can we model complex connections between intentions, space, and time? How can we use the spatial context to make the inference process more efficient? (section 3) 3. After the game: how can the spatial data collected during the game help us to improve geographic data quality? Can we use LBGs for the community-based collection of geographic data? (section 4) In this project report we will only shortly review the main findings of the first research direction and then concentrate on the current project phase which is concerned with the second and third. These two directions are closely related to ongoing PhD research projects. We present the central findings of previous and current work, and conclude our report in section 5. Figure 1: Board game Tic Tac Toe (left) and the spatialized version GeoTicTacToe (right)