Artificial Intelligence for Modeling Complex Systems: Taming the Complexity of Expert Models to Improve Decision Making. Gil, Y., Garijo, D., Khider, D., Knoblock, C. A., Ratnakar, V., Osorio, M., Vargas, H., Pham, M., Pujara, J., Shbita, B., Vu, B., Chiang, Y., Feldman, D., Lin, Y., Song, H., Kumar, V., Khandelwal, A., Steinbach, M., Tayal, K., Xu, S., Pierce, S. A., Pearson, L., Hardesty-Lewis, D., Deelman, E., Silva, R. F. D., Mayani, R., Kemanian, A. R., Shi, Y., Leonard, L., Peckham, S., Stoica, M., Cobourn, K., Zhang, Z., Duffy, C., & Shu, L. ACM Transactions on Interactive Intelligent Systems, 11(2):11:1–11:49, July, 2021.
Artificial Intelligence for Modeling Complex Systems: Taming the Complexity of Expert Models to Improve Decision Making [link]Paper  doi  abstract   bibtex   25 downloads  
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.
@article{gil_artificial_2021,
	title = {Artificial {Intelligence} for {Modeling} {Complex} {Systems}: {Taming} the {Complexity} of {Expert} {Models} to {Improve} {Decision} {Making}},
	volume = {11},
	issn = {2160-6455},
	shorttitle = {Artificial {Intelligence} for {Modeling} {Complex} {Systems}},
	url = {https://doi.org/10.1145/3453172},
	doi = {10.1145/3453172},
	abstract = {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.},
	number = {2},
	urldate = {2021-09-09},
	journal = {ACM Transactions on Interactive Intelligent Systems},
	author = {Gil, Yolanda and Garijo, Daniel and Khider, Deborah and Knoblock, Craig A. and Ratnakar, Varun and Osorio, Maximiliano and Vargas, Hernán and Pham, Minh and Pujara, Jay and Shbita, Basel and Vu, Binh and Chiang, Yao-Yi and Feldman, Dan and Lin, Yijun and Song, Hayley and Kumar, Vipin and Khandelwal, Ankush and Steinbach, Michael and Tayal, Kshitij and Xu, Shaoming and Pierce, Suzanne A. and Pearson, Lissa and Hardesty-Lewis, Daniel and Deelman, Ewa and Silva, Rafael Ferreira Da and Mayani, Rajiv and Kemanian, Armen R. and Shi, Yuning and Leonard, Lorne and Peckham, Scott and Stoica, Maria and Cobourn, Kelly and Zhang, Zeya and Duffy, Christopher and Shu, Lele},
	month = jul,
	year = {2021},
	keywords = {Intelligent user interfaces, integrated modeling, model metadata, regional-level decision-making, remote sensing data},
	pages = {11:1--11:49},
}
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