Transfer of evolved pattern-based heuristics in games

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 neuro-evolution 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.