Goal recognisers attempt to infer an agent's intentions from a sequence of observations. Approaches that adapt classical planning techniques to goal recognition have previously been proposed but, generally, they assume the initial world state is accurately defined. In this paper, a state is inaccurate if any fluent's value is unknown or incorrect. To cope with this, a cyclic Action Graph, which models the order constraints between actions, is traversed to label each node with their distance from each hypothesis goal. These distances are used to calculate the posterior goal probabilities. Our experimental results, for 15 different domains, demonstrate that our approach is unaffected by an inaccurately defined initial state.