Modeling human behavior during emergency evacuation using intelligent agents: A multi-agent simulation approach

It is costly and takes a lot of time for disaster employees to execute several evacuation drills for a building. One cannot glean information to advance the plan and blueprint of forthcoming buildings without executing many drills. We have developed a multi-agent system simulation application to aid in running several evacuation drills and theoretical situations. This paper combines the genetic algorithm (GA) with neural networks (NNs) and fuzzy logic (FL) to explore how intelligent agents can learn and adapt their behavior during an evacuation. The adaptive behavior focuses on the specific agents changing their behavior in the environment. The shared behavior of the agent places an emphasis on the crowd-modeling and emergency behavior in the multi-agent system. This paper provides a fuzzy individual model being developed for realistic modeling of human emotional behavior under normal and emergency conditions. It explores the impact of perception and emotions on the human behavior. We have established a novel intelligent agent with characteristics such as independence, collective ability, cooperativeness, and learning, which describes its final behavior. The contributions of this paper lie in our approach of utilizing a GA, NNs, and FL to model learning and adaptive behavior of agents in a multi-agent system. The planned application will help in executing numerous evacuation drills for what-if scenarios for social and cultural issues such as evacuation by integrating agent characteristics. This paper also compares our proposed multi-agent system with existing commercial evacuation tools as well as real-time evacuation drills for accuracy, building traffic characteristics, and the cumulative number of people exiting during evacuation. Our results show that the inclusion of GA, NNs, and fuzzy attributes made the evacuation time of the agents closer to the real-time evacuation drills.

[1]  Ajay Singh Yadav,et al.  An Optimal Ordering Policy for Non-instantaneous Deteriorating Items with Conditionally Permissible Delay in Payment under Two Storage Management , 2016 .

[2]  Harold E. Nelson,et al.  Federal investigation of the evacuation of the World Trade Center on September 11, 2001: FEDERAL INVESTIGATION OF THE EVACUATION OF THE WORLD TRADE CENTER , 2012 .

[3]  Joseph Bates,et al.  The role of emotion in believable agents , 1994, CACM.

[4]  T. Arai,et al.  Social human behavior modeling for robot imitation learning , 2012, 2012 IEEE International Conference on Mechatronics and Automation.

[5]  Grant R. Gerhart,et al.  Simulation of convoy of unmanned vehicles using agent based modeling , 2007, SPIE Security + Defence.

[6]  Jason D. Averill,et al.  Federal investigation of the evacuation of the World Trade Center on September 11, 2001 , 2007 .

[7]  Shen-Wen Chien,et al.  AN EVACUATION SIMULATION MODEL (ESM) FOR BUILDING EVALUATION , 2005 .

[8]  Nicholas R. Jennings,et al.  On agent-based software engineering , 2000, Artif. Intell..

[9]  Pankaj Mehra.  Context-Aware Computing: Beyond Search and Location-Based Services , 2012, IEEE Internet Comput..

[10]  Sarit Kraus,et al.  Multiagent Negotiation under Time Constraints , 1995, Artif. Intell..

[11]  Sarit Kraus,et al.  A Kernel-Oriented Model for Coalition-Formation in General Environments: Implementation and Results , 1996, AAAI/IAAI, Vol. 1.

[12]  Lin Padgham,et al.  User understanding of cognitive processes in simulation: A tool for exploring and modifying , 2012, Proceedings Title: Proceedings of the 2012 Winter Simulation Conference (WSC).

[13]  Edwin R. Galea,et al.  The UK 9/11 evacuation study: Analysis of survivors’ recognition and response phase in WTC1 , 2010 .

[14]  Moshe Tennenholtz,et al.  On the Synthesis of Useful Social Laws for Artificial Agent Societies (Preliminary Report) , 1992, AAAI.

[15]  Nitish Chooramun.  Implementing a hybrid spatial discretisation within an agent based evacuation model , 2011 .

[16]  Enrico Ronchi,et al.  Social influence on route choice in a virtual reality tunnel fire , 2014 .

[17]  Thiago Tinoco Pires,et al.  An approach for modeling human cognitive behavior in evacuation models , 2005 .

[18]  T. Vicsek,et al.  Simulation of pedestrian crowds in normal and evacuation situations , 2002 .

[19]  Sharad Sharma,et al.  AvatarSim: A multi-agent system for emergency evacuation simulation , 2009, J. Comput. Methods Sci. Eng..

[20]  Sharad Sharma.  Use of Favorite Goal in Agent Based Modeling and Simulation , 2012, Int. J. Comput. Their Appl..

[21]  Ajith Abraham,et al.  Opposition based Chaotic Differential Evolution algorithm for solving global optimization problems , 2012, 2012 Fourth World Congress on Nature and Biologically Inspired Computing (NaBIC).

[22]  Sharad Sharma,et al.  Fuzzy Approach for Predicting Probability of Reaching a Target in a Battlefield Environment , 2010, Int. J. Comput. Their Appl..

[23]  Van Troi Tran.  More than just another crowd, we need a waiting line instead , 2013 .

[24]  S. H. Choi,et al.  Performance optimisation of mobile robots in dynamic environments , 2012, 2012 IEEE International Conference on Virtual Environments Human-Computer Interfaces and Measurement Systems (VECIMS) Proceedings.

[25]  Victor R. Lesser,et al.  Coalitions Among Computationally Bounded Agents , 1997, Artif. Intell..

[26]  Håkan Frantzich,et al.  Fire alarm in a public building : how do people evaluate information and choose evacuation exit? , 1999 .

[27]  Karen Boyce,et al.  Refuge areas and vertical evacuation of multistorey buildings: the end users' perspectives , 2015 .

[28]  Erica D. Kuligowski,et al.  Stairwell Evacuation from Buildings: What We Know We Don't Know , 2009 .

[29]  S. Sharma,et al.  Intelligent agents in a goal finding application for homeland security , 2012, 2012 Proceedings of IEEE Southeastcon.

[30]  John H. Holland,et al.  Hidden Order: How Adaptation Builds Complexity , 1995 .

[31]  Robyn R. M. Gershon,et al.  The World Trade Center evacuation study: Factors associated with initiation and length of time for evacuation , 2012 .

[32]  S. Sharma,et al.  Multi-agent modeling and simulation of human behavior in aircraft evacuations , 2008, IEEE Transactions on Aerospace and Electronic Systems.