In an attempt to understand and emulate intelligent behavior Artificial Intelligence researchers have, for the most part, taken a reductionist approach and divided their investigation into separate studies of reason, perception, and action. As a consequence, intelligent robots have been constructed using a coarse grained architecture; reasoning, perception, and action have been implemented as separate modules that interact infrequently. This paper describes an investigation into the effect of reducing this architecture granularity on the computational efficiency of the overall system. It demonstrates that introducing a fine grained integration or `interweaving' of these functions can result in significant complexity reduction. This paper introduces the `reason a little, move a little, look a little,' or RML paradigm, describes an RML navigation system, and discusses analytical and experimental results that quantify complexity reduction for planning and vision. The system details illustrate novel approaches to representation, planning, and vision. The environment is represented as a network that provides mechanisms for coping with positional uncertainty and focusing reasoning activities. Plans are constructed in three dimensions using a geometry-induced hierarchical decomposition. The approach to vision takes its lead from the way a blind man uses his cane: to verity that reason is consistent with reality.