As autonomous agents enter complex environments, it becomes more difficult to adequately model the interactions between the two. Agents must therefore cope with greater ambiguity (e.g., unknown environments, underdefined models, and vague problem definitions). Despite the consequences of ignoring ambiguity, tools for decision making under ambiguity are understudied. The general approach has been to avoid ambiguity (exploit known information) using robust methods. This work contributes ambiguity attitude graph search (AAGS), generalizing robust methods with ambiguity attitudes--the ability to trade-off between seeking and avoiding ambiguity in the problem. AAGS solves online decision making problems with limited budget to learn about their environment. To evaluate this approach AAGS is tasked with path planning in static and dynamic environments. Results demonstrate that appropriate ambiguity attitudes are dependent on the quality of information from the environment. In relatively certain environments, AAGS can readily exploit information with robust policies. Conversely, model complexity reduces the information conveyed by individual samples; this allows the risks taken by optimistic policies to achieve better performance.

翻译：随着自主智能体进入复杂环境，对两者互动的充分建模变得愈发困难。智能体因此必须应对更大的模糊性（例如未知环境、欠定义模型及模糊问题定义）。尽管忽视模糊性会带来严重后果，但针对模糊性下的决策工具研究仍显不足。主流方法倾向于采用鲁棒方法来规避模糊性（利用已知信息）。本研究提出模糊态度图搜索（AAGS），通过引入模糊态度——即权衡问题中寻求与规避模糊性的能力——对鲁棒方法进行泛化。AAGS能够在有限预算下解决在线决策问题以学习环境信息。为评估该方法，我们令AAGS在静态与动态环境中执行路径规划任务。结果表明，恰当的模糊态度取决于环境信息的质量：在相对确定的环境中，AAGS可借助鲁棒策略有效利用信息；反之，模型复杂度降低了个体样本所传达的信息，使得乐观策略承担的风险能够达到更优性能。