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A Novel Framework for Characterizing Exposure-Related Behaviors Using Agent-Based Models Embedded with Needs-Based Artificial Intelligence (CSSSA2016)
Brandon, N., K. Dionisio, K. Isaacs, D. Kapraun, Woodrow Setzer, AND R. Tornero-Velez. A Novel Framework for Characterizing Exposure-Related Behaviors Using Agent-Based Models Embedded with Needs-Based Artificial Intelligence (CSSSA2016). The Computational Social Science Society of the Americas, Santa Fe, NM, November 17 - 20, 2016.
Preset NERL's research on the use of agent based modeling in exposure assessments. To obtain feed back on the approach from the leading experts in the field.
Descriptions of where and how individuals spend their time are important for characterizing exposures to chemicals in consumer products and in indoor environments. Herein we create an agent-based model (ABM) that is able to simulate longitudinal patterns in behaviors. By basing our ABM upon a needs-based artificial intelligence (AI) system, we create agents that mimic human decisions on these exposure-relevant behaviors. In a case study of adults, we use the AI to predict the inter-individual variation in the start time and duration of four behaviors: sleeping, eating, commuting, and working. The results demonstrate that the ABM can capture both inter-individual variation and how decisions on one behavior can affect subsequent behaviors.
Record Details:Record Type: DOCUMENT (PRESENTATION/POSTER)
Organization:U.S. ENVIRONMENTAL PROTECTION AGENCY
OFFICE OF RESEARCH AND DEVELOPMENT
NATIONAL EXPOSURE RESEARCH LABORATORY
COMPUTATIONAL EXPOSURE DIVISION