Resumen
This paper describes a new model to automatically generating dynamic formation strategies for robotic soccer applications based on game conditions, regarded to as favorable or unfavorable for a robotic team. Decisions are distributedly computed by the players of a multi-agent team. A game policy is defined and applied by a human coach who establishes the attitude of the team for defending or attacking. A simple neural net model is applied using current and previous game experience to classify the game's parameters so that the new game conditions can be determined so that a robotic team can modify its strategy on-the-fly. Experiments and results of the proposed model for a robotic soccer team show the promise of the approach.
Idioma original | Inglés |
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Páginas (desde-hasta) | 6082-6090 |
Número de páginas | 9 |
Publicación | Expert Systems with Applications |
Volumen | 36 |
N.º | 3 PART 2 |
DOI | |
Estado | Publicada - abr. 2009 |
Publicado de forma externa | Sí |