An integrated human decision making model for evacuation scenarios under a BDI framework

Abstract

An integrated Belief-Desire-Intention (BDI) modeling framework is proposed for human decision making and planning for evacuation scenarios, whose submodules are based on a Bayesian Belief Network (BBN), Decision-Field-Theory (DFT), and a Probabilistic Depth-First Search (PDFS) 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. To mimic realistic human behaviors, attributes of the BDI framework are reverse-engineered from human-in-the-loop experiments conducted in the Cave Automatic Virtual Environment (CAVE). The proposed modeling framework is demonstrated for a human's evacuation behaviors in response to a terrorist bomb attack. The simulated environment and agents (models of humans) conforming to the proposed BDI framework are implemented in AnyLogic® agent-based simulation software, where each agent 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 simulation has been used to test the impact of several factors (e.g., demographics, number of police officers, information sharing via speakers) on evacuation performance (e.g., average evacuation time, percentage of casualties).