Artificial Intelligence for Modeling Complex Systems: Taming the Complexity of Expert Models to Improve Decision Making
Autor: | Varun Ratnakar, Deborah Khider, Yijun Lin, Maximiliano Osorio, Daniel Garijo, Zeya Zhang, Scott D. Peckham, Rajiv Mayani, Jay Pujara, Rafael Ferreira da Silva, Dan Feldman, Yao-Yi Chiang, Daniel Hardesty-Lewis, Basel Shbita, Yuning Shi, Craig A. Knoblock, Lele Shu, Michael Steinbach, Maria Stoica, Kshitij Tayal, Lissa Pearson, Kelly M. Cobourn, Ankush Khandelwal, Vipin Kumar, Yolanda Gil, Lorne Leonard, Suzanne A. Pierce, Binh Vu, Armen R. Kemanian, Shaoming Xu, Ewa Deelman, Christopher J. Duffy, Minh Pham, Hernán Vargas, Hayley Song |
---|---|
Rok vydání: | 2021 |
Předmět: |
010504 meteorology & atmospheric sciences
Human systems engineering business.industry Computer science 0207 environmental engineering Complex system 02 engineering and technology 01 natural sciences Variety (cybernetics) Human-Computer Interaction Metadata Intelligent user interface Artificial Intelligence Selection (linguistics) Frame (artificial intelligence) Relevance (information retrieval) Artificial intelligence 020701 environmental engineering business 0105 earth and related environmental sciences |
Zdroj: | ACM Transactions on Interactive Intelligent Systems. 11:1-49 |
ISSN: | 2160-6463 2160-6455 |
Popis: | 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. |
Databáze: | OpenAIRE |
Externí odkaz: |