Interacting with Artificial Intelligence (AI) profoundly changes the nature of human activity as well as the subjective experience that agents have of their own actions and their consequences. We propose to mitigate this effect by making AI systems more intelligible to human operators. We hypothesized that the readability of system intentions is a key element of their predictability and, by extension, of the human operator's abilities to interact effectively with highly automated systems. We conducted experiments to explore the impact of the communication of AI intentions during joint human-AI interaction (intention-based explanations). Trust human operators’ have towards such algorithms as well as their sense of control across different dimensions (performance, action fluency, contribution) was measured. Overall, our results suggest that adding intention-based explanations during human-AI interaction indeed support cooperation between the human operator and artificial agents.