Integrated human decision making model under Belief-Desire-Intention framework for crowd simulation

Abstract

An integrated Belief-Desire-Intention (BDI) modeling framework is proposed for human decision making and planning, whose sub-modules are based on Bayesian belief network (BBN), Decision-Field-Theory (DFT), and probabilistic depth first search (PDFS) technique. 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). The proposed modeling framework is demonstrated for human?s evacuation behaviors under a terrorist bomb attack situation. The simulated environment and agents (human model) 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 impact of several factors (e.g. demographics of people, number of policemen) on evacuation performance (e.g. average evacuation time, percentage of casualties).