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.