Measuring Control to Dynamically Induce Flow in Tetris.
Autor: Lora-Ariza, Diana Sofía; Sánchez Rubio, Antonio A.; González-Calero, Pedro Antonio; Camps Ortueta, Irene
Resumen: Dynamic Difficulty Adjustment (DDA) is a set of
techniques that aim to automatically adapt the difficulty of
a video game based on the player’s performance. This paper
presents a methodology for DDA using ideas from the theory of
flow and case-based reasoning (CBR). In essence we are looking
to generate game sessions with a similar difficulty evolution to
previous game sessions that have produced flow in players with
a similar skill level. We propose a CBR approach to dynamically
assess the player’s skill level and adapt the difficulty of the game
based on the relative complexity of the last game states.
We develop a DDA system for Tetris using this methodology
and show, in a experiment with 40 participants, that the DDA
version has a measurable impact on the perceived flow using
validated questionnaires.
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