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
