A Framework for Optimal Sequential Planning in Multiagent Settings

In this thesis, we present a new framework called Interactive POMDPS (I-POMDPs) for optimal planning by an agent interacting with other autonomous agents in a sequential environment and maximizing its reward that depends on joint actions of all agents.