1
|
Gil Y, Garijo D, Khider D, Knoblock CA, Ratnakar V, Osorio M, Vargas H, Pham M, Pujara J, Shbita B, Vu B, Chiang YY, Feldman D, Lin Y, Song H, Kumar V, Khandelwal A, Steinbach M, Tayal K, Xu S, Pierce SA, Pearson L, Hardesty-Lewis D, Deelman E, Silva RFD, Mayani R, Kemanian AR, Shi Y, Leonard L, Peckham S, Stoica M, Cobourn K, Zhang Z, Duffy C, Shu L. Artificial Intelligence for Modeling Complex Systems: Taming the Complexity of Expert Models to Improve Decision Making. ACM T INTERACT INTEL 2021. [DOI: 10.1145/3453172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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.
Collapse
Affiliation(s)
- Yolanda Gil
- Information Sciences Institute, University of Southern California, Marina del Rey, CA 90292
| | - Daniel Garijo
- Information Sciences Institute, University of Southern California, Marina del Rey, CA 90292
| | - Deborah Khider
- Information Sciences Institute, University of Southern California, Marina del Rey, CA 90292
| | - Craig A. Knoblock
- Information Sciences Institute, University of Southern California, Marina del Rey, CA 90292
| | - Varun Ratnakar
- Information Sciences Institute, University of Southern California, Marina del Rey, CA 90292
| | - Maximiliano Osorio
- Information Sciences Institute, University of Southern California, Marina del Rey, CA 90292
| | - Hernán Vargas
- Information Sciences Institute, University of Southern California, Marina del Rey, CA 90292
| | - Minh Pham
- Information Sciences Institute, University of Southern California, Marina del Rey, CA 90292
| | - Jay Pujara
- Information Sciences Institute, University of Southern California, Marina del Rey, CA 90292
| | - Basel Shbita
- Information Sciences Institute, University of Southern California, Marina del Rey, CA 90292
| | - Binh Vu
- Information Sciences Institute, University of Southern California, Marina del Rey, CA 90292
| | - Yao-Yi Chiang
- Spatial Sciences Institute, University of Southern California, Los Angeles, CA 90089
| | - Dan Feldman
- Spatial Sciences Institute, University of Southern California, Los Angeles, CA 90089
| | - Yijun Lin
- Spatial Sciences Institute, University of Southern California, Los Angeles, CA 90089
| | - Hayley Song
- Spatial Sciences Institute, University of Southern California, Los Angeles, CA 90089
| | - Vipin Kumar
- Department of Computer Science, University of Minnesota, Minneapolis, MN 55455
| | - Ankush Khandelwal
- Department of Computer Science, University of Minnesota, Minneapolis, MN 55455
| | - Michael Steinbach
- Department of Computer Science, University of Minnesota, Minneapolis, MN 55455
| | - Kshitij Tayal
- Department of Computer Science, University of Minnesota, Minneapolis, MN 55455
| | - Shaoming Xu
- Department of Computer Science, University of Minnesota, Minneapolis, MN 55455
| | - Suzanne A. Pierce
- Texas Advanced Computing Center, University of Texas at Austin, Austin, TX 78758
| | - Lissa Pearson
- Texas Advanced Computing Center, University of Texas at Austin, Austin, TX 78758
| | | | - Ewa Deelman
- Information Sciences Institute, University of Southern California, Marina del Rey, CA 90292
| | | | - Rajiv Mayani
- Information Sciences Institute, University of Southern California, Marina del Rey, CA 90292
| | - Armen R. Kemanian
- Department of Plant Science, The Pennsylvania State University, University Park, PA 16802
| | - Yuning Shi
- Department of Plant Science, The Pennsylvania State University, University Park, PA 16802
| | - Lorne Leonard
- Department of Plant Science, The Pennsylvania State University, University Park, PA 16802
| | - Scott Peckham
- Institute of Arctic and Alpine Research, University of Colorado, Boulder, CO 80309
| | - Maria Stoica
- Institute of Arctic and Alpine Research, University of Colorado, Boulder, CO 80309
| | - Kelly Cobourn
- Department of Forest Resources and Environmental Conservation, Virginia Tech, Blacksburg, VA 24061
| | - Zeya Zhang
- Department of Forest Resources and Environmental Conservation, Virginia Tech, Blacksburg, VA 24061
| | - Christopher Duffy
- Department of Civil Engineering, The Pennsylvania State University, University Park, PA 16802
| | - Lele Shu
- Department of Land, Air and Water Resources, University of California Davis, Davis, CA 95616
| |
Collapse
|