Major societal and environmental challenges involve complex systems that have diverse multi-scale interacting processes. Consider, for example, how droughts and water reserves affect crop production and how agriculture and industrial needs affect water quality and availability. Preventive measures, such as delaying planting dates and adopting new agricultural practices in response to changing weather patterns, can reduce the damage caused by natural processes. Understanding how these natural and human processes affect one another allows forecasting the effects of undesirable situations and study interventions to take preventive measures. For many of these processes, there are expert models that incorporate state-of-the-art theories and knowledge to quantify a system's response to a diversity of conditions. A major challenge for efficient modeling is the diversity of modeling approaches across disciplines and the wide variety of data sources available only in formats that require complex conversions. Using expert models for particular problems requires integration of models with third-party data as well as integration of models across disciplines. Modelers face significant heterogeneity that requires resolving semantic, spatiotemporal, and execution mismatches, which are largely done by hand today and may take more than 2 years of effort. We are developing a modeling framework that uses artificial intelligence (AI) techniques to reduce modeling effort while ensuring utility for decision making. Our work to date makes several innovative contributions: (1) an intelligent user interface that guides analysts to frame their modeling problem and assists them by suggesting relevant choices and automating steps along the way; (2) semantic metadata for models, including their modeling variables and constraints, that ensures model relevance and proper use for a given decision-making problem; and (3) semantic representations of datasets in terms of modeling variables that enable automated data selection and data transformations. This framework is implemented in the MINT (Model INTegration) framework, and currently includes data and models to analyze the interactions between natural and human systems involving climate, water availability, agricultural production, and markets. Our work to date demonstrates the utility of AI techniques to accelerate modeling to support decision-making and uncovers several challenging directions for future work.

中文翻译：

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用于复杂系统建模的人工智能：驯服专家模型的复杂性以改进决策制定

主要的社会和环境挑战涉及具有多种多尺度相互作用过程的复杂系统。例如，考虑干旱和水资源储备如何影响作物生产，以及农业和工业需求如何影响水质和可用性。预防措施，例如推迟种植日期和采用新的农业做法以应对不断变化的天气模式，可以减少自然过程造成的损害。了解这些自然和人类过程如何相互影响可以预测不良情况的影响并研究干预措施以采取预防措施。对于其中许多过程，有专家模型结合了最先进的理论和知识来量化系统对各种条件的响应。高效建模的一个主要挑战是跨学科建模方法的多样性以及仅以需要复杂转换的格式提供的各种数据源。为特定问题使用专家模型需要将模型与第三方数据集成以及跨学科的模型集成。建模者面临着需要解决语义、时空和执行不匹配的显着异质性，这在今天主要是手工完成的，可能需要 2 年多的时间。我们正在开发一个建模框架，该框架使用人工智能 (AI) 技术来减少建模工作，同时确保对决策的实用性。迄今为止，我们的工作做出了一些创新贡献：(1) 一个智能用户界面，指导分析师构建他们的建模问题，并通过建议相关选择和自动化步骤来帮助他们；(2) 模型的语义元数据，包括它们的建模变量和约束，确保模型相关性和对给定决策问题的正确使用；(3) 数据集的语义表示在能够实现自动数据选择和数据转换的建模变量方面。该框架在 MINT（Model INTegration）框架中实施，目前包括数据和模型，用于分析涉及气候、水资源可用性、农业生产和市场的自然和人类系统之间的相互作用。