A method for automatic situation recognition in collaborative multiplayer Serious Games

Abstract

One major Serious Games challenge is adaptation of game-based learning environments towards the needs of players with heterogeneous player and learner traits. For both an instructor or an algorithmic adaptation mechanism it is vital to have knowledge about the course of the game in order to be able to recognize player intentions, potential problems, or misunderstandings - both of the game(play) and the learning content. The main contribution of this paper is a mechanism to recognize high-level situations in a multiplayer Serious Game. The approach presented uses criteria and situations based on the game-state, player actions and events and calculates how likely it is that players are in a certain situation. The gathered information can be used to feed an adaptation algorithm or be presented to the instructor to improve instructor decision making. In a first evaluation, the situation recognition was able to correctly recognize all of the situations in a set of game sessions. Thus, the contribution of this paper contains a novel approach to automatically capture complex multiplayer game states influenced by unpredictable player behavior, and to interpret that information to calculate probabilities of relevant game situations to be present from which player intentions can be derived.

Keywords

• Serious Games
• Collaborative Learning
• Game Mastering
• Adaptation