Abstract
Imitative learning can be considered an essential task of humans development. People use instructions and demonstrations provided by other human experts to acquire knowledge. In order to make an agent capable of learning through demonstrations, we propose a relational framework for learning by imitation. Demonstrations and domain specific knowledge are compactly represented by a logical language able to express complex relational processes. The agent interacts in a stochastic environment and incrementally receives demonstrations. It actively interacts with the human by deciding the next action to execute and requesting demonstration from the expert based on the current learned policy. The framework has been implemented and validated with experiments in simulated agent domains.
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Bombini, G., Di Mauro, N., Basile, T.M.A., Ferilli, S., Esposito, F. (2009). Relational Learning by Imitation. In: Håkansson, A., Nguyen, N.T., Hartung, R.L., Howlett, R.J., Jain, L.C. (eds) Agent and Multi-Agent Systems: Technologies and Applications. KES-AMSTA 2009. Lecture Notes in Computer Science(), vol 5559. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01665-3_28
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DOI: https://doi.org/10.1007/978-3-642-01665-3_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01664-6
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