This position paper reflects on the state-of-the-art in decision-making under uncertainty. A classical assumption is that probabilities can sufficiently capture all uncertainty in a system. In this paper, the focus is on the uncertainty that goes beyond this classical interpretation, particularly by employing a clear distinction between aleatoric and epistemic uncertainty. The paper features an overview of Markov decision processes (MDPs) and extensions to account for partial observability and adversarial behavior. These models sufficiently capture aleatoric uncertainty but fail to account for epistemic uncertainty robustly. Consequently, we present a thorough overview of so-called uncertainty models that exhibit uncertainty in a more robust interpretation. We show several solution techniques for both discrete and continuous models, ranging from formal verification, over control-based abstractions, to reinforcement learning. As an integral part of this paper, we list and discuss several key challenges that arise when dealing with rich types of uncertainty in a model-based fashion.

翻译：本立场论文反思了不确定性下决策的现有技术发展水平。经典假设认为概率足以完全捕捉系统中的所有不确定性。本文聚焦于超越这一经典解释的不确定性，特别通过明确区分偶然不确定性和认知不确定性展开讨论。文章概述了马尔可夫决策过程（MDP）及其扩展模型，以应对部分可观测性和对抗行为。这类模型虽能充分捕捉偶然不确定性，但在鲁棒处理认知不确定性方面存在不足。为此，我们全面综述了所谓的不确定性模型——这些模型以更鲁棒的解读方式呈现不确定性。我们展示了针对离散和连续模型的多种求解技术，涵盖形式化验证、基于控制的抽象方法以及强化学习。作为本文的核心组成部分，我们列举并讨论了以模型为基础处理丰富类型不确定性时面临的若干关键挑战。