Due to the complexity of many decision making problems, tree search algorithms often have inadequate information to produce accurate transition models. Robust methods, designed to make safe decisions when faced with these uncertainties, often overlook the impact expressions of uncertainty have on how the decision is made. This work introduces the Ambiguity Attitude Graph Search (AAGS), advocating for more precise representation of ambiguities (uncertainty from a set of plausible models) 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 valid alternatives. Simulation in a dynamic sailing environment shows how highly stochastic environments 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.

翻译：由于许多决策问题的复杂性，树搜索算法通常缺乏足够的信息来生成精确的转移模型。鲁棒方法旨在面对这些不确定性时做出安全决策，但往往忽略了不确定性的表达方式对决策过程的影响。本研究引入模糊态度图搜索（AAGS），倡导在决策中更精确地表示模糊性（来自一组合理模型的不确定性）。此外，AAGS允许用户调整其模糊态度（或偏好），这有助于促进探索，并提升用户控制智能体在面对一组有效替代方案时应如何响应的能力。在动态航行环境中的模拟显示，高度随机的环境可能导致鲁棒方法失效。结果进一步表明，调整模糊态度能更好地实现目标，同时缓解鲁棒方法的这种失效模式。由于本方法是鲁棒框架的泛化，这些结果还表明，关注模糊性的算法在安全关键系统之外也具有适用性。