dc.contributor.author | Lora-Ariza, Diana Sofía | |
dc.contributor.author | Sánchez Rubio, Antonio A. | |
dc.contributor.author | González-Calero, Pedro Antonio | |
dc.contributor.author | Camps Ortueta, Irene | |
dc.date.accessioned | 2023-02-01T12:38:13Z | |
dc.date.available | 2023-02-01T12:38:13Z | |
dc.date.issued | 2022 | |
dc.identifier.issn | 2475-1502 | spa |
dc.identifier.uri | https://hdl.handle.net/10641/3236 | |
dc.description.abstract | 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. | spa |
dc.language.iso | eng | spa |
dc.publisher | IEEE Transactions on Games | spa |
dc.rights | Atribución-NoComercial-SinDerivadas 3.0 España | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ | * |
dc.subject | Dynamic Difficulty Adjustment | spa |
dc.subject | Artificial intelligence | spa |
dc.subject | Video games | spa |
dc.title | Measuring Control to Dynamically Induce Flow in Tetris. | spa |
dc.type | journal article | spa |
dc.type.hasVersion | SMUR | spa |
dc.rights.accessRights | open access | spa |
dc.description.extent | 456 KB | spa |
dc.identifier.doi | 10.1109/TG.2022.3182901 | spa |
dc.relation.publisherversion | https://ieeexplore.ieee.org/document/9795354 | spa |