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Conventional models of navigation commonly assume a navigation agent’s location can be precisely determined. This paper examines the more general case, where an agent’s actual location cannot be precisely determined. This paper develops a formal model of navigation under imprecision using a graph. Two key strategies for dealing with imprecision are identified and defined: contingency and refinement. A contingency strategy aims to find an instruction sequence that maximizes an agent’s chances of reaching its destination. A refinement strategy aims to use knowledge gained as an agent moves through the network to disambiguate location. Examples of both strategies are empirically tested using a simulation with computerized navigation agents moving through a road network at different levels of locational imprecision. The results of the simulation indicate that both the strategies, contingency and refinement, applied individually can produce significant improvements in navigation performance under imprecision, at least at relatively fine granularities. Using both strategies in concert produced significant improvements in performance across all granularities.