Due to the complexity of many decision making problems, tree search algorithms often have inadequate information to produce accurate transition models. This results in ambiguities (uncertainties for which there are multiple plausible models). Faced with ambiguities, robust methods have been used to produce safe solutions--often by maximizing the lower bound over the set of plausible transition models. However, they often overlook how much the representation of uncertainty can impact how a decision is made. This work introduces the Ambiguity Attitude Graph Search (AAGS), advocating for more comprehensive representations of ambiguities in decision making. Additionally, AAGS allows users to adjust their ambiguity attitude (or preference), promoting exploration and improving users' ability to control how an agent should respond when faced with a set of plausible alternatives. Simulation in a dynamic sailing environment shows how environments with high entropy transition models can lead robust methods to fail. Results further demonstrate how adjusting ambiguity attitudes better fulfills objectives while mitigating this failure mode of robust approaches. Because this approach is a generalization of the robust framework, these results further demonstrate how algorithms focused on ambiguity have applicability beyond safety-critical systems.