Dificulty Adjustment in Tetris with Time Series

Keeping a player within the flow in a game is a central goal for game designers, making the game neither too easy nor too hard. Dynamic Difficulty adjustment seeks to fulfill this goal by dynamically tuning difficulty according to player actions in the game. In this paper we demonstrate that case-based reasoning with time series can serve to automatically identify the ability of a player in a game, and thus serve as the input for difficulty adjustment based on player ability. We try different configurations for the generation of the case base from game logs, and compare them in terms of how well they classify player ability.