Grazia Bombini, Nicola Di Mauro, Teresa M. A. Basile, Stefano Ferilli, Floriana Esposito



Humans use imitation as a mechanism for acquiring knowledge, i.e. they use instructions and/or demonstrations provided by other humans. In this paper we propose a logic programming framework for learning from imitation in order to make an agent able to learn from relational demonstrations. In particular, demonstrations are received in incremental way and used as training examples while the agent interacts in a stochastic environment. This logical framework allows to represent domain specific knowledge as well as to compactly and declaratively represent complex relational processes. The framework has been implemented and validated with experiments in simulated agent domains.


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Paper Citation

in Harvard Style

Bombini G., Di Mauro N., M. A. Basile T., Ferilli S. and Esposito F. (2009). A LOGIC PROGRAMMING FRAMEWORK FOR LEARNING BY IMITATION . In Proceedings of the 11th International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-8111-85-2, pages 218-223. DOI: 10.5220/0002007502180223

in Bibtex Style

author={Grazia Bombini and Nicola Di Mauro and Teresa M. A. Basile and Stefano Ferilli and Floriana Esposito},
booktitle={Proceedings of the 11th International Conference on Enterprise Information Systems - Volume 2: ICEIS,},

in EndNote Style

JO - Proceedings of the 11th International Conference on Enterprise Information Systems - Volume 2: ICEIS,
SN - 978-989-8111-85-2
AU - Bombini G.
AU - Di Mauro N.
AU - M. A. Basile T.
AU - Ferilli S.
AU - Esposito F.
PY - 2009
SP - 218
EP - 223
DO - 10.5220/0002007502180223