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KESAMSTA
2009
Springer

Relational Learning by Imitation

10 years 1 months ago
Relational Learning by Imitation
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. Key words: Relational Learning, Learning by Imitation, Agents
Grazia Bombini, Nicola Di Mauro, Teresa Maria Alto
Added 27 May 2010
Updated 27 May 2010
Type Conference
Year 2009
Where KESAMSTA
Authors Grazia Bombini, Nicola Di Mauro, Teresa Maria Altomare Basile, Stefano Ferilli, Floriana Esposito
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