A major goal of Artificial Intelligence is designing agents: systems that perceive their environment and execute actions. In particular, a fundamental question is how to build intelligent agents. When uncertainty and many agents are involved, this question is particularly challenging, and has not yet been answered in a satisfactory way.The need for scalable and flexible multiagent planning is particularly pressing given that intelligent distributed systems are becoming ubiquitous in society. For instance, autonomous guided vehicles transport cargo and people, inter-vehicle communication allow cars to form vehicular networks, smart grid infrastructure allows consumers to produce and sell electricity, and surveillance cameras provide urban security and safety. In these settings, the controller of the vehicle, the consumers, or the controller of the cameras all need to act in the face of uncertainty.For an agent in isolation, planning under uncertainty has been studied using decision-theoretic models like Partially Observable Markov Decision Processes (POMDPs). Such single-agent, centralized methods clearly do not suffice for large-scale multiagent systems. I focus on multiagent techniques, and I propose to advance the state of the art as follows.First, instead of the rigid history-based plans currently in use, I will develop a flexible plan representation with methods for determining the impact on other agents of updating one agent's plan. Second, I will consider scenarios with self-interested agents, relevant in domains such as smart grids or cars driving on a highway. I will be able to scale up non-cooperative techniques by exploiting local interactions between agents. These advances will be empirically tested in intelligent transportation systems and smart grids.Moving to TU Delft will allow me to learn from their expertise on re-planning, and on planning with self-interested agents, as well as provide unique opportunities for the empirical evaluation.