Problems where agents wish to cooperate for a common goal, but disagree on their view of reality are frequent. Of particular interest are settings where one agent is an AI ``helper agent'' and the other is a human. The AI wants to help the human to complete a task but the AI and human may disagree about the world model. This may come about for example because of the limited rationality and biases of the human or because of misaligned reward models. In this paper, we formalize this as the multi-view sequential game, and show that even when the human's model is far from correct, an AI can still steer their behavior to more beneficial outcomes. In particular, we develop a number of algorithms, based on dynamic programming to discover helper policies for the AI, under different assumptions about the AI's knowledge. Experimentally, we show that the AI's beliefs about human model are not required to be accurate in order to act as a useful helper agent.