Social interaction implies exchange between two or more persons, where they adapt their behaviors to each others. With the growing interest in human-agent interactions, it is desirable to make these interactions natural and human like. In this context, we aimed at enhancing the quality of the interaction between users and Embodied Conversational Agents ECAs by (1) endowing the ECA with the capacity to express social attitudes, such as being friendly or dominant depending its role or relationship with its interaction partners; (2) adapting the agent's behavior according to the user's behavior, hence, the conversation partners influence each others through an interaction loop, thus, enhancing the interaction quality; (3) predicting the user's engagement level and adapting the agent's behavior accordingly. We take advantage of the recent advances in machine learning, more specifically, temporal sequence mining and neural networks to model these capacities in the ECA. The first model is used to learn relevant patterns (sequences) of non-verbal signals that best represent attitude variations, and then reproduce them on the agent. The latter is used to encompass the dynamics of non-verbal signals. Two use cases have been explored using the well-known LSTM model: agent's behavior adaptation based on both agent's and user's behavior history, and user's engagement prediction based on his/her own behavior history. The implemented models and algorithms have been validated through a number of perceptive studies as well as through rigorous quantitative analysis of the obtained results. In addition, the realized models have been integrated into a virtual-agent platform.