Trace-based decision making in interactive application: Case of Tamagotchi systems

We present our exploratory work for situation preselecting in interactive applications, assuming that the application is an Interactive Adaptive System based on a sequence of contextualized “situations”. Each situation confines activities and interactions related to a common context, resources and system actors. When one situation is completed, the system has to determine which is the best following one. We introduce in this paper a new preselecting method that identifies possible next situations among all available situations. We propose a strategy using Naïve Bayes based on the analysis of the sets of available traces (the past of users). Combining all obtained results, we get a set of situations, called set of alternatives that can be used in any decision algorithm. We demonstrate our approach on a case study based on Tamagotchi game.