Dynamic Learning in Human Decision Behavior for Evacuation Scenarios under BDI Framework Seungho Lee, Young-Jun Son. 2009 INFORMS at University of Warwick, June 25-27, Coventry, U.K.

A novel approach to represent learning in human decision behavior for evacuation scenarios is proposed under the context of an extended Belief-Desire-Intention framework. In particular, we focus on how a human adjusts his perception process (involving a Bayesian belief network) in Belief Module dynamically against his performance in predicting the environment as part of his decision planning function. To this end, a Q-learning algorithm (reinforcement learning algorithm) is employed and further developed. In this work, the human decision behavior model is implemented in AnyLogic agent-based simulation software, and the constructed simulation is used to test the impact of the proposed learning approach on emergency evacuation performance, and initial results look quite promising.

Dynamic Learning in Human Decision Behavior for Evacuation Scenarios under BDI Framework

Figure 1: Components of the extended BDI framework

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