research-article

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

Authors:

Judy JinAuthors Info & Claims

ACM Transactions on Modeling and Computer Simulation (TOMACS), Volume 20, Issue 4

Article No.: 23, Pages 1 - 24

https://doi.org/10.1145/1842722.1842728

Published: 05 November 2010 Publication History

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).

References

[1]

Busemeyer, J. R., and Diederich, A. 2002. Survey of decision field theory. Math. Social Sci. 43, 345--370.

[2]

Busemeyer, J.R., and Townsend, J.T. 1993. Decision field theory: A dynamic-cognitive approach to decision making in an uncertain environment. Psychol. Rev. 100, 3, 432--459.

[3]

Earnshaw, R.A., Vince J.A, and Jones H. 1995. Virtual Reality Applications. Academic Press Ltd., London, UK.

Digital Library

[4]

Edwards, W. 1954. The theory of decision making. Psychol. Bull. 51, 4, 380--417.

[5]

Einhorn, H.J. 1970. The use of nonlinear, noncompensatory models in decision making. Psychol. Bull. 73, 3, 221--230.

[6]

Gao, J., and Lee, J.D. 2006. Extending the decision field theory to model operator's reliance on automation in supervisory control situations. IEEE Trans. Syst. Man Cybernet. Part A 36, 5, 943--959.

Digital Library

[7]

Gibson, F.P., Fichman, M., and Plaut, D.C. 1997. Learning in dynamic decision tasks: Computational model and empirical evidence. Organiz. Behav. Hum. Decis. Process. 71, 1--35.

[8]

Glimcher, P.W. 2003. Decision, Uncertainty, and the Brain, The Science of Neuroeconomics. MIT Press, Cambridge, MA.

[9]

Hamagami T., and Hirata H. 2003. Method of crowd simulation by using multiagent on cellular automata. In Proceedings of IEEE/WIC International Conference on Intelligent Agent Technology (IAT'03). 46--52.

Digital Library

[10]

Kinny, D., Georgeff, M., and Rao, A. 1996. A methodology and modeling technique for systems of BDI agents. In Proceedings of the 7th European Workshop on Modeling Autonomous Agents in a Multi-Agent World MAAMAW'96. W. Van Der Velde and J.W. Perram, Eds. Springer Verlag, 56--71.

Digital Library

[11]

Konar, A., and Chakraborty, U.K. 2005. Reasoning and unsupervised learning in a fuzzy cognitive map. Infor. Sci. 170, 419--441.

Digital Library

[12]

Laird, J.E., NewelL, A., and Rosenbloom, P.S. 1987. Soar: An architecture for general intelligence. Artif. Intell. 33, 1--64.

Digital Library

[13]

Lee, S., Son, Y., and Jin, J. 2008. Decision field theory extensions for behavior modeling in dynamic environment using Bayesian belief network. Infor. Sci. 178, 10, 2297--2314.

Digital Library

[14]

Mosteller, F., and Nogee, P. 1951. An experimental measurement of utility. J. Polit. Econ. 59, 371--404.

[15]

Newell, A. 1990. Unified Theories of Cognition. Harvard University Press, Cambridge, MA.

Digital Library

[16]

Norling, E. 2004. Folk psychology for human modeling: Extending the BDI paradigm. In Proceedings of the International Conference on Autonomous Agents and Multi-Agent System, 202--209.

Digital Library

[17]

Opaluch, J.J., and Segerson, K. 1989. Rational roots of irrational behavior: New theories of economic decision-making. Northeastern J. Agricul. Resource Econ. 18, 2, 81--95.

[18]

Payne, J.W. 1982. Contingent decision behavior. Psychol. Bull. 92, 382--402.

[19]

Rao, A.S., and Georgeff, M.P. 1998. Decision procedures for BDI logics. J. Logic Comput. 8, 3, 293--343.

[20]

Rothrock, L., and Yin, J. 2008. Integrating compensatory and noncompensatory decision making strategies in dynamic task environments. In Decision Modeling and Behavior in Uncertain and Complex Environments, T. Kugler et al., Eds. Springer, 123--138.

[21]

Sanfey, A.G., Loewenstein, G., Mcclure, S.M., and Cohen, J.D. 2006. Neuroeconomics: Cross-Currents in research on decision-making. TRENDS Cogn. Sci. 10, 3, 108--116.

[22]

Sen, S., Askin, R., Bahill, T., Jin, J., Smith, C., Son, Y., and Szidarovszky, F. 2008. Predicting and prescribing human decision making under uncertain and complex scenarios. MURI (award number: F49620-03-1-0377) 2007 annual report.

[23]

Shendarkar, A., Vasudevan, K., Lee, S., and Son, Y. 2006. Crowd simulation for emergency response using BDI agents based on immersive virtual reality. Simul. Model. Prac. Theory 16, 1415--1429.

[24]

Shizgal, P. 1997. Neural basis of utility estimation. Current Opin. Neurobiol. 7, 198--208.

[25]

Simon, H.A. 1955. A behavioral model of rational choice. The Quar. J. Econ. 69, 99--118.

[26]

Sirbiladze, G., and Gachechiladze, T. 2005. Restored fuzzy measures in expert decision-making. Inf. Sci. 169, 71--95.

Digital Library

[27]

Zhao, X., and Son, Y. 2008. BDI-based human decision-making model in automated manufacturing systems. Int. J. Mode. Simul. 28, 3, 347--356.

Cited By

Sun HHan GZhang XRuan X(2025)Grasping emergency dynamics: A review of group evacuation techniques and strategies in major emergenciesJournal of Safety Science and Resilience10.1016/j.jnlssr.2024.05.0066:1(1-20)Online publication date: Mar-2025
https://doi.org/10.1016/j.jnlssr.2024.05.006
Pogmore ADavies RCooke N(2024)Virtual Versus Reality: A Systematic Review of Real-World Built Environment Tasks Performed in CAVEs and a Framework for Performance and Experience EvaluationVirtual Worlds10.3390/virtualworlds30400283:4(536-571)Online publication date: 20-Nov-2024
https://doi.org/10.3390/virtualworlds3040028
KARADUMAN BTEZEL BCHALLENGER M(2023)Bulanık Mantığın Akıllı Etmenlere Bütünleştirilmesi: Bir SFS Üzerinde DeneylerIntegrating Fuzzy Logic into Intelligent Agents: Experiments on a CPSTürkiye Bilişim Vakfı Bilgisayar Bilimleri ve Mühendisliği Dergisi10.54525/tbbmd.103528716:1(34-44)Online publication date: 29-Jun-2023
https://doi.org/10.54525/tbbmd.1035287
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An integrated human decision making model for evacuation scenarios under a BDI framework

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Published In

cover image ACM Transactions on Modeling and Computer Simulation

ACM Transactions on Modeling and Computer Simulation Volume 20, Issue 4

October 2010

155 pages

ISSN:1049-3301

EISSN:1558-1195

DOI:10.1145/1842722

Issue’s Table of Contents

Copyright © 2010 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 05 November 2010

Accepted: 01 December 2009

Revised: 01 December 2009

Received: 01 April 2008

Published in TOMACS Volume 20, Issue 4

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Cited By

Sun HHan GZhang XRuan X(2025)Grasping emergency dynamics: A review of group evacuation techniques and strategies in major emergenciesJournal of Safety Science and Resilience10.1016/j.jnlssr.2024.05.0066:1(1-20)Online publication date: Mar-2025
https://doi.org/10.1016/j.jnlssr.2024.05.006
Pogmore ADavies RCooke N(2024)Virtual Versus Reality: A Systematic Review of Real-World Built Environment Tasks Performed in CAVEs and a Framework for Performance and Experience EvaluationVirtual Worlds10.3390/virtualworlds30400283:4(536-571)Online publication date: 20-Nov-2024
https://doi.org/10.3390/virtualworlds3040028
KARADUMAN BTEZEL BCHALLENGER M(2023)Bulanık Mantığın Akıllı Etmenlere Bütünleştirilmesi: Bir SFS Üzerinde DeneylerIntegrating Fuzzy Logic into Intelligent Agents: Experiments on a CPSTürkiye Bilişim Vakfı Bilgisayar Bilimleri ve Mühendisliği Dergisi10.54525/tbbmd.103528716:1(34-44)Online publication date: 29-Jun-2023
https://doi.org/10.54525/tbbmd.1035287
Li DYang JLi JZhao NJu WGuo M(2023)Agent-Based Modeling and Simulation (ABMS)on the influence of adjusting medical service fees on patients' choice of medical treatmentBMC Health Services Research10.1186/s12913-023-09933-323:1Online publication date: 30-Aug-2023
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Ebrahimi HSattari FLefsrud LMacciotta R(2023)A machine learning and data analytics approach for predicting evacuation and identifying contributing factors during hazardous materials incidents on railwaysSafety Science10.1016/j.ssci.2023.106180164(106180)Online publication date: Aug-2023
https://doi.org/10.1016/j.ssci.2023.106180
Hassanpour SGonzalez VZou YLiu JWang FCastillo ECabrera-Guerrero G(2023)Incorporation of BIM-based probabilistic non-structural damage assessment into agent-based post-earthquake evacuation simulationAdvanced Engineering Informatics10.1016/j.aei.2023.10195856:COnline publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1016/j.aei.2023.101958
Zhang WWu CYou XKust LChen YShi J(2022)Communication Between Automated Vehicles and Drivers in Manual Driving Vehicles: Using a Mechanical Arm to Produce GesturesInternational Journal of Human–Computer Interaction10.1080/10447318.2022.208202239:13(2644-2655)Online publication date: 12-Jun-2022
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Wang DLiang SChen BWu C(2022)Investigation on the impacts of natural lighting on occupants' wayfinding behavior during emergency evacuation in underground spaceEnergy and Buildings10.1016/j.enbuild.2021.111613255(111613)Online publication date: Jan-2022
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Taylor SSon YBranke JRose OSanchez S(2021)Using simulation and artificial intelligence to innovateProceedings of the Winter Simulation Conference10.5555/3522802.3523024(1-9)Online publication date: 13-Dec-2021
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E Taylor SSon YBranke JRose OSanchez S(2021)Using Simulation and Artificial Intelligence to Innovate: Are We Getting Even Smarter?2021 Winter Simulation Conference (WSC)10.1109/WSC52266.2021.9715402(1-9)Online publication date: 12-Dec-2021
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