A common approach to the robotic path planning problem is to discretize the search space and plan an optimal path using graph search methods. As the optimality criteria becomes more complex it becomes increasingly difficult to optimize the search with respect to each metric being considered. A hybrid path planning system is introduced that supplements the graph search approach with a cognitive architecture. The selected cognitive architecture, Soar, will provide high level reasoning, guiding the implementation of the graph search and reasoning on the results generated. Given scenario-specific context, the hybrid approach will allow for multiple, complex and possibly conflicting criteria to be considered during path generation.