Learning is key to achieving human-level intelligence. Transferring knowledge that is learned on one task to another one speeds up learning in the target task by exploiting the relevant prior knowledge. As a test case, this study introduces a method to transfer local pattern-based heuristics from a simple board game to a more complex one. The patterns are generated by compositional pattern producing networks (CPPNs), which are evolved with the NEAT neuroevolution method. Results show that transfer improves both final performance and the total learning time, compared to evolving patterns for the target game from scratch. Pattern-based transfer is therefore a promising approach to scaling up game players toward human-level.

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To appear in Proceedings of the IEEE Symposium on Computational Intelligence and Games (CIG), 2008.