We discuss an integrated Belief-Desire-Intention (BDI) modeling framework for human decision making, whose sub-modules are based on Bayesian belief network, Decision-Field-Theory, and probabilistic depth first search technique. A key novelty of the proposed model is its ability to represent both the human decision-making and decision-planning functions in a unified framework. In this talk, the proposed modeling framework is demonstrated for human’s evacuation behaviors under a terrorist bomb attack situation. To mimic realistic human behaviors, attributes of the BDI framework are reverse-engineered from the human-in-the-loop experiments conducted in the Cave Automatic Virtual Environment (CAVE) available at The University of Arizona. A crowd simulation is then constructed, where individual human behaviors are based on what was learned from the CAVE experiments. In this work, the simulated environment and humans conforming to the proposed BDI framework are implemented in AnyLogic® agent-based simulation software, where each human entity calls external Netica BBN software to perform its perceptual processing function and Soar software to perform its real-time planning and decision-execution functions. The constructed crowd simulation is then used to test impact of several factors (e.g. demographics of people, number of policemen, information sharing via speakers) on evacuation performance (e.g. average evacuation time, percentage of casualties). Finally, we briefly discuss other applications (e.g. driver’s behaviors) and research extensions for the proposed BDI framework.
Dr. Young-Jun Son is a Professor and the Head of Systems and Industrial Engineering Department, and the Director of Center for Advanced Integration of Manufacturing Systems and Technologies (AIMST) at The University of Arizona. He is the Editor-in-Chief of International Journal of Services Operations and Informatics, a Department Editor of the IIE Transactions, and serve on the editorial board for six other international journals. He is an IIE Fellow, and has received several research awards such as the SME 2004 Outstanding Young Manufacturing Engineer Award, the IIE 2005 Outstanding Young Industrial Engineer Award, the Industrial Engineering Research Conference Best Paper Award (in 2005, 2008, and 2009), and the Best Paper of the Year Award (2007) in International Journal of Industrial Engineering.