Abstract
One of the most important problems for development of intelligent agents is adaptation to the environment. In this paper we briefly describe Helli-Respina soccer simulator team that uses a new self-adaptive method named Dynamic Multi-Behavior Assessment (DMBA). By using built-in behavior manager named dynamic behavior transformer method lets the agent can choose the best algorithms to decide during the game. This system always tries to choose a set of available algorithms to get the best result against each opponent. The main objective in this research is how to choose a set of algorithms dynamically to get the best result against an opponent.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Bahador Nooraei B., Omid Aladini and Siavash Rahbar N.: Dynamic multi-behavior assessment:an approach to unsupervised machine learning, (2001)
Peter Stone, Manuela Veloso and Patrick Riley: The CMUnited99 soccer simulator team, (1999)
Hironori Aoyagi, Hiroki Shimora, Takuya Morishita, Tomomi Kawarabayashi, Takenori Kubo, Kyouiti Hiroshima, Junji Nishino: The Zeng 99 soccer simulator team, (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bahador Nooraei, B., Siavash Rahbar, N., Aladini, O. (2002). Helli-Respina 2001 Team Description Paper. In: Birk, A., Coradeschi, S., Tadokoro, S. (eds) RoboCup 2001: Robot Soccer World Cup V. RoboCup 2001. Lecture Notes in Computer Science(), vol 2377. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45603-1_73
Download citation
DOI: https://doi.org/10.1007/3-540-45603-1_73
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-43912-7
Online ISBN: 978-3-540-45603-2
eBook Packages: Springer Book Archive