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Inductive transfer refers to the ability of a learning mechanism to improve performance on the current task after having learned a different but related concept or skill on a previous task. Transfer may additionally occur between two or more learning tasks that are being undertaken concurrently. Transfer may include background knowledge or a particular form of search bias.
As an illustration, an application of inductive transfer arises in competitive games involving teams of robots (e.g., Robocup Soccer). In this scenario, transferring knowledge learned from one task into another task is crucial to acquire skills necessary to beat the opponent team. Specifically, imagine a situation where a team of robots has been taught to keep a soccer ball away from the opponent team. To achieve that goal, robots must learn to keep the ball, pass the ball to a close teammate, etc., always trying to remain at a safe distance from the opponents....
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Recommended Reading
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Vilalta, R., Giraud-Carrier, C., Brazdil, P., Soares, C. (2011). Inductive Transfer. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-30164-8_401
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DOI: https://doi.org/10.1007/978-0-387-30164-8_401
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