An Integrated Human Decision Making Model under Extended Belief-Desire-Intention Framework

Abstract

In this keynote talk, we discuss an extended Belief-Desire-Intention (BDI) framework for human decision making and planning, whose sub-modules have been developed using Bayesian belief network (BBN), Decision-Field-Theory (DFT), and a probabilistic depth first search (DFS) technique. A key novelty of the proposed approach is its ability to represent both the human decision-making as well as decision-planning functions in a coherent framework. In this talk, the proposed framework is illustrated and demonstrated for human's evacuation behaviors under a terrorist bomb attack situation. To mimic realistic human decision-planning and decision-making behaviors, attributes of the extended BDI framework are calibrated 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 modeled based on what was learned from the CAVE experiments. In this work, the simulated environment (e.g. streets and buildings) and humans conforming to the extended 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 used to evaluate the impact of several factors (e.g. number of policemen, information sharing via speakers/mobile phones) on the evacuation performance. Finally, we briefly discuss other applications (e.g. driver's behaviors) and research extensions (e.g. human learning/forgetting and human interactions) for the extended BDI framework.