It is a vital behaviour pattern of humans, the most highly developed autonomous agents, to make use of experiences accumulated in the past and to solve new problems in analogy to solutions of old, yet similar problems. This report gives an outline of our work to apply that case-based approach to an artificial agent in the domain of Robotic Soccer simulation. We enable the online coach of a robotic soccer team to determine the team line-up by a technique that incorporates knowledge into its reasoning process that was gained from former soccer matches. In order to use the knowledge contained in old cases, it is indispensable to define a meaningful evaluation of old solutions. Moreover, it is necessary to retrieve and adapt those solutions whose application to the current problem situation promises to be most auspicious. For these reasons, we also concentrate on the assessment of a team's performance. Further, we focus on a most precise calculation of the similarity between heterogeneous player types.

49 pages

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