Una metodología de ajuste dinámico de dificultad en videojuegos.Entre Rubber Band AI y la teoría de flow

  1. LORA ARIZA, DIANA SOFÍA
Dirigée par:
  1. Antonio Alejandro Sánchez Ruiz-Granados Directeur
  2. Pedro González Calero Directeur/trice

Université de défendre: Universidad Complutense de Madrid

Fecha de defensa: 18 juillet 2022

Jury:
  1. María Belén Díaz Agudo President
  2. Carlos León Aznar Secrétaire
  3. Antonio Mora García Rapporteur
  4. Raúl Lara Cabrera Rapporteur
  5. Antonio José Fernández Leiva Rapporteur

Type: Thèses

Résumé

One of the most relevant challenges during the video games production is the creation of adequate challenges for the players. Normally, the production of an AAA video game takes approximately 2 years and the main reason is that the ideas to be deveoped must be tested, not only to find bugs in the code, but also to validate that these are entertaining and geasible. This work aims to create a methodology that facilitates the establishment of appropriate challenges using dynamic difficulty adjustment in video games. After reviewing the literature on playre modelling and dynamic difficulty adjustment, we conducted several experiments where participants played different versions of Tetris. The version that implemented the proposed methodology predicted the current player's skill level by extracting data from his last actions and compared the evolution of the game with other player's games. Then, it decided ehether to modify the game's difficulty by considering the previously calculated skill level. We used two dynamic difficulty adjustment approaches, the Rubber Band AI and the flow theory, to implement the various versions of Tetris. In addition, participants answered questionnaires to identify if their experience was satisfactory in each session...