Abstract
This paper describes the scoring policy which is used by the agents of the UvA Trilearn simulation team. In a given situation this policy enables an agent to determine the best shooting point in the goal, together with an associated probability of scoring when the ball is shot to this point. Our policy is based on an approximate method for learning the relevant statistics of the ball motion which can be regarded as a geometrically constrained continuous-time Markov process. © Springer-Verlag Berlin Heidelberg 2003.
Cite
CITATION STYLE
Kok, J., De Boer, R., Vlassis, N., & Groen, F. C. A. (2003). Towards an optimal scoring policy for simulated soccer agents. In Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) (Vol. 2752, pp. 296–303). Springer Verlag. https://doi.org/10.1007/978-3-540-45135-8_24
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
