We consider an agent that seeks to make abstract predictions about the world by only distinguishing between certain features of observations. Making accurate abstract predictions of this form may not require a fully detailed model of the world, though in general it will require that the agent make some finer distinctions than those that interest it. We assume the agent has a partition of the observation space of a dynamical system induced by the features of interest. The goal of this paper is to find a minimal refinement of that partition such that a model of the refined system will make accurate predictions with respect to the features of interest. We provide algorithms and worst-case bounds on the difficulty of performing this task. Our results apply generally to all discrete dynamical systems.
['Erik Talvitie', 'Britton Wolfe', 'Satinder P. Singh']
Building Incomplete but Accurate Models