Lora-Ariza, Diana SofíaSánchez Rubio, Antonio A.González-Calero, Pedro AntonioCamps Ortueta, Irene2023-02-012023-02-0120222475-1502https://hdl.handle.net/10641/3236Dynamic 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.engAtribución-NoComercial-SinDerivadas 3.0 Españahttp://creativecommons.org/licenses/by-nc-nd/3.0/es/Dynamic Difficulty AdjustmentArtificial intelligenceVideo gamesjournal articleopen access10.1109/TG.2022.3182901