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.
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