Artificial Intelligence (AI) systems planned for deployment in real-world applications frequently are researched and developed in closed simulation environments where all variables are controlled and known to the simulator or labeled benchmark datasets are used. Transition from these simulators, testbeds, and benchmark datasets to more open-world domains poses significant challenges to AI systems, including significant increases in the complexity of the domain and the inclusion of real-world novelties; the open-world environment contains numerous out-of-distribution elements that are not part in the AI systems' training set. Here, we propose a path to a general, domain-independent measure of domain complexity level. We distinguish two aspects of domain complexity: intrinsic and extrinsic. The intrinsic domain complexity is the complexity that exists by itself without any action or interaction from an AI agent performing a task on that domain. This is an agent-independent aspect of the domain complexity. The extrinsic domain complexity is agent- and task-dependent. Intrinsic and extrinsic elements combined capture the overall complexity of the domain. We frame the components that define and impact domain complexity levels in a domain-independent light. Domain-independent measures of complexity could enable quantitative predictions of the difficulty posed to AI systems when transitioning from one testbed or environment to another, when facing out-of-distribution data in open-world tasks, and when navigating the rapidly expanding solution and search spaces encountered in open-world domains.

翻译：人工智能（AI）系统在规划部署于实际应用时，通常是在封闭的仿真环境中进行研究和开发，其中所有变量均受控且已知于仿真器，或使用带标注的基准数据集。从这些仿真器、测试平台和基准数据集过渡到更开放的世界领域，给AI系统带来了显著挑战，包括领域复杂度的显著增加以及现实世界新颖性的纳入；开放世界环境包含大量不属于AI系统训练集的分布外元素。本文提出了一种通用的、与领域无关的领域复杂度水平度量路径。我们区分了领域复杂度的两个方面：内在复杂度与外在复杂度。内在领域复杂度是独立存在、无需AI智能体在该领域执行任务时进行任何操作或交互的复杂度，这是领域复杂度的智能体无关方面。外在领域复杂度则依赖于智能体与任务。内在与外在元素共同捕捉了领域的整体复杂度。我们以领域无关的视角构建了定义和影响领域复杂度水平的组成部分。领域无关的复杂度度量能够实现对AI系统在不同测试平台或环境间过渡、在开放世界任务中面对分布外数据、以及在探索开放世界领域快速扩展的解空间和搜索空间时，所面临困难程度的定量预测。