Agent-Based Modeling in Hierarchical Control of Swarms During Evacuation

We address the problem of evacuation from the perspective of agent-based modeling (ABM) in this paper. The evacuation problem is modeled as a navigation of multiple agents that spatially interact with each other in a known environment. The environment is divided into a danger and a safe zone while t...

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Published inSN computer science Vol. 4; no. 1; p. 38
Main Authors Janovská, Kristýna, Surynek, Pavel
Format Journal Article
LanguageEnglish
Published Singapore Springer Nature Singapore 01.11.2022
Springer Nature B.V
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ISSN2661-8907
2662-995X
2661-8907
DOI10.1007/s42979-022-01437-x

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Abstract We address the problem of evacuation from the perspective of agent-based modeling (ABM) in this paper. The evacuation problem is modeled as a navigation of multiple agents that spatially interact with each other in a known environment. The environment is divided into a danger and a safe zone while the task of agents is to move from the danger zone to the safe one in a collision-free manner. Unlike previous approaches that model the environment as a discrete graph with agents placed in its vertices, at most one agent per vertex, our approach adopts various continuous aspects such as a grid-based embedding of the environment into 2D space and continuous line of sight of agents. In addition to this, we adopt hierarchical structure of multi-agent system in which so called leading agents are more informed having full knowledge of other agents and are capable of performing multi-agent pathfinding (MAPF) via centralized algorithms like conflict-based search (CBS) while so called follower agents with limited knowledge about other agents are modeled using simple local rules. Our experimental evaluation indicates that suggested hierarchical modeling approach can serve as a tool for studying the progress and the efficiency of evacuation processes in different environments.
AbstractList We address the problem of evacuation from the perspective of agent-based modeling (ABM) in this paper. The evacuation problem is modeled as a navigation of multiple agents that spatially interact with each other in a known environment. The environment is divided into a danger and a safe zone while the task of agents is to move from the danger zone to the safe one in a collision-free manner. Unlike previous approaches that model the environment as a discrete graph with agents placed in its vertices, at most one agent per vertex, our approach adopts various continuous aspects such as a grid-based embedding of the environment into 2D space and continuous line of sight of agents. In addition to this, we adopt hierarchical structure of multi-agent system in which so called leading agents are more informed having full knowledge of other agents and are capable of performing multi-agent pathfinding (MAPF) via centralized algorithms like conflict-based search (CBS) while so called follower agents with limited knowledge about other agents are modeled using simple local rules. Our experimental evaluation indicates that suggested hierarchical modeling approach can serve as a tool for studying the progress and the efficiency of evacuation processes in different environments.
We address the problem of evacuation from the perspective of agent-based modeling (ABM) in this paper. The evacuation problem is modeled as a navigation of multiple agents that spatially interact with each other in a known environment. The environment is divided into a danger and a safe zone while the task of agents is to move from the danger zone to the safe one in a collision-free manner. Unlike previous approaches that model the environment as a discrete graph with agents placed in its vertices, at most one agent per vertex, our approach adopts various continuous aspects such as a grid-based embedding of the environment into 2D space and continuous line of sight of agents. In addition to this, we adopt hierarchical structure of multi-agent system in which so called leading agents are more informed having full knowledge of other agents and are capable of performing multi-agent pathfinding (MAPF) via centralized algorithms like conflict-based search (CBS) while so called follower agents with limited knowledge about other agents are modeled using simple local rules. Our experimental evaluation indicates that suggested hierarchical modeling approach can serve as a tool for studying the progress and the efficiency of evacuation processes in different environments.
ArticleNumber 38
Author Surynek, Pavel
Janovská, Kristýna
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Cites_doi 10.1145/3384441.3395973
10.1016/j.proeng.2016.01.072
10.1007/978-3-642-31500-8_59
10.1145/3007120.3007143
10.1007/BF02993491
10.1109/TSSC.1968.300136
10.1016/j.patcog.2021.107885
10.5220/0008071501370143
10.1109/ROBOT.2009.5152326
10.1016/j.asoc.2018.04.015
10.1609/aiide.v1i1.18726
10.5220/0010678200003064
10.5220/0005104802490254
10.5220/0010167700170027
10.1016/j.ejor.2018.01.050
10.1016/j.artint.2014.11.006
10.1109/TCSS.2017.2783332
10.1016/j.cja.2020.03.006
10.1609/aaai.v24i1.7564
10.1093/ietisy/e89-d.8.2372
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References Even C, Pillac V, Hentenryck PV. NICTA evacuation planner: actionable evacuation plans with contraflows. In: ECAI 2014—21st European Conference on Artificial Intelligence, 18–22 August 2014, Prague, Czech Republic—including prestigious applications of intelligent systems (PAIS 2014). Frontiers in artificial intelligence and applications, 2014;263:1143–48. IOS Press.
Surynek P. A novel approach to path planning for multiple robots in bi-connected graphs. In Proceedings of the 2009 IEEE International Conference on Robotics and Automation, ICRA 2009, Kobe, Japan, May 12–17, IEEE; 2009. pp. 3613–3619
WilenskyURandWAn Introduction to agent-based modeling: modeling natural, social, and engineered complex systems with NetLogo2015CambridgeThe MIT Press
Ng C, Chow W. A brief review on the time line concept in evacuation. 2006. https://www.bse.polyu.edu.hk/researchcentre/fire_engineering/summary_of_output/journal/IJAS/V7/p1-13.pdf. Accessed Oct 2021.
Zia K, Ferscha A. An agent-based model of crowd evacuation: combining individual, social and technological aspects. In: Proceedings of the 2019 ACM SIGSIM Conference on principles of advanced discrete simulation, SIGSIM-PADS 2020, Miami, FL, USA, June 15–17, ACM; 2020. pp. 129–140.
Liu C, li Mao Z, min Fu Z. Emergency evacuation model and algorithm in the building with several exits. In: Procedia Engineering 2015 International Conference on performance-based fire and fire protection engineering (ICPFFPE 2015)2016;135:12–18.
Chen L, Tang TQ, Song Z, Huang HJ, Guo RY. Child behavior during evacuation under non-emergency situations: experimental and simulation results 2018. https://www.sciencedirect.com/science/article/abs/pii/S1569190X18301539. Accessed Oct 2021.
Bazior G, Palka D, Was J. Cellular automata based modeling of competitive evacuation. In: Cellular automata–13th International Conference on cellular automata for research and industry, ACRI 2018, Como, Italy, September 17–21, 2018, Proceedings. Lecture Notes in Computer Science, vol. 11115, 2018; p. 451–459. Springer.
LiuHXuBLuDZhangGA path planning approach for crowd evacuation in buildings based on improved artificial bee colony algorithmAppl Soft Comput20186836037610.1016/j.asoc.2018.04.015
Sikora W, Malinowski J, Kupczak A. Model of skyscraper evacuation with the use of space symmetry and fluid dynamic approximation. In: Parallel processing and applied mathematics—9th International Conference, PPAM 2011, Torun, Poland, September 11–14, 2011. Revised Selected Papers, Part II. Lecture Notes in Computer Science, vol 7204. Springer; 2011. pp. 570–77.
Zhang J, Li C, Kosov S, Grzegorzek M, Shirahama K, Jiang T, Sun C, Li Z, Li H. Lcu-net: A novel low-cost u-net for environmental microorganism image segmentation. Pattern Recogn. 2021;115. https://doi.org/10.1016/j.patcog.2021.107885, www.sciencedirect.com/science/article/pii/S0031320321000728. Accessed Oct 2021.
Malcolm Ryan. Graph decomposition for efficient multi-robot path planning. In: IJCAI 2007, Proceedings of the 20th International Joint Conference on artificial intelligence, Hyderabad, India, January 6–12, 2007; pp. 2003–2008, 2007.
ChalmetLGFrancisRLSaundersPBNetwork models for building evacuationFire Technol19821819011310.1007/BF02993491
KurdiHAAl-MegrenSAlthunyanRAlmulifiAEffect of exit placement on evacuation plansEur J Oper Res2018269274975910.1016/j.ejor.2018.01.0501388.90124
KamiyamaNKatohNTakizawaAAn efficient algorithm for evacuation problem in dynamic network flows with uniform arc capacityIEICE Trans Inf Syst200689–D82372237910.1093/ietisy/e89-d.8.23721137.90348
Silver D. Cooperative pathfinding. In: Proceedings of the First Artificial Intelligence and Interactive Digital Entertainment Conference, June 1–5, 2005, Marina del Rey, California, USA: AAAI Press; 2005. pp. 117–122.
HartPENilssonNJRaphaelBA formal basis for the heuristic determination of minimum cost pathsIEEE Trans Syst Sci Cybern SSC19684210010710.1109/TSSC.1968.300136
Sharon G, Stern R, Felner A, Sturtevant N.R. Conflict-based search for optimal multi-agent pathfinding. Artificial Intelligence, vol. 219. Elsevier; 2014. pp. 40–66.
Standley T. S. Finding optimal solutions to cooperative pathfinding problems. In: Fox M, Poole D. editors. Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2010, Atlanta, Georgia, USA, July 11–15, AAAI Press; 2010. pp. 173–178
RussellSNorvigPArtificial intelligence: a modern approach20103Pearson Education0835.68093
Mas E, Koshimura S, Imamura F, Suppasri A, Muhari A, Adriano B. Recent advances in agent-based tsunami evacuation simulations: case studies in Indonesia, Thailand, Japan and Peru 2015. https://link.springer.com/article/10.1007/s00024-015-1105-y. Accessed Oct 2021.
TrivediARaoSAgent-based modeling of emergency evacuations considering human panic behaviorIEEE Trans Comput Soc Syst20185127728810.1109/TCSS.2017.2783332
Tsurushima A. Reproducing evacuation behaviors of evacuees during the great east japan earthquake using the evacuation decision model with realistic settings. In: Rocha A. P., Steels L, van den Herik H. J.. editors. Proceedings of the 13th International Conference on Agents and Artificial Intelligence, ICAART 2021, Volume 1, Online Streaming, February 4–6, 2021; p. 17–27. SCITEPRESS https://doi.org/10.5220/0010167700170027, https://doi.org/10.5220/0010167700170027. Accessed Oct 2021.
Arbib C, Muccini H, Moghaddam MT. Applying a network flow model to quick and safe evacuation of people from a building: a real case. In: Proceedings of the GEOSAFE Workshop on robust solutions for fire fighting, CEUR Workshop Proceedings 2146 (RSFF 2018), L’Aquila, Italy July 19–20, 2018; pp. 50–61.
Janovská K, Surynek P. Hierarchical control of swarms during evacuation. In: Proceedings of the 13th International Joint Conference on knowledge discovery, knowledge engineering and knowledge management—Volume 2: KEOD, INSTICC, SciTePress, 2021; p. 61–73. https://doi.org/10.5220/0010678200003064.
Zafar M, Zia K, Muhammad A, Ferscha A. An agent-based model of crowd evacuation integrating agent perception and proximity pressure. In: Proceedings of the 14th International Conference on advances in mobile computing and multi media, MoMM 2016, Singapore, November 28–30, ACM; 2016. pp. 12–19.
Selvek R, Surynek P. Engineering smart behavior in evacuation planning using local cooperative path finding algorithms and agent-based simulations. In: Proceedings of the 11th International Joint Conference on knowledge discovery, knowledge engineering and knowledge management—Volume 2: KEOD, 2019; p. 137–143. INSTICC, SciTePress. https://doi.org/10.5220/0008071501370143.
Chen H, Wang X, Shen L, Cong Y. Formation flight of fixed-wing uav swarms: a group-based hierarchical approach. Chin J Aeronaut. 2020. https://doi.org/10.1016/j.cja.2020.03.006, www.sciencedirect.com/science/article/pii/S1000936120301205. Accessed Oct 2021.
Galea E, Blake S, Lawrence P, Gwynne S. The airexodus evacuation model and its application to aircraft safety. Proceedings of the FAA/JAA conference, Atlantic City, October 2001, FAA/JAA.
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References_xml – reference: Janovská K, Surynek P. Hierarchical control of swarms during evacuation. In: Proceedings of the 13th International Joint Conference on knowledge discovery, knowledge engineering and knowledge management—Volume 2: KEOD, INSTICC, SciTePress, 2021; p. 61–73. https://doi.org/10.5220/0010678200003064.
– reference: Sikora W, Malinowski J, Kupczak A. Model of skyscraper evacuation with the use of space symmetry and fluid dynamic approximation. In: Parallel processing and applied mathematics—9th International Conference, PPAM 2011, Torun, Poland, September 11–14, 2011. Revised Selected Papers, Part II. Lecture Notes in Computer Science, vol 7204. Springer; 2011. pp. 570–77.
– reference: HartPENilssonNJRaphaelBA formal basis for the heuristic determination of minimum cost pathsIEEE Trans Syst Sci Cybern SSC19684210010710.1109/TSSC.1968.300136
– reference: Galea E, Blake S, Lawrence P, Gwynne S. The airexodus evacuation model and its application to aircraft safety. Proceedings of the FAA/JAA conference, Atlantic City, October 2001, FAA/JAA.
– reference: Liu C, li Mao Z, min Fu Z. Emergency evacuation model and algorithm in the building with several exits. In: Procedia Engineering 2015 International Conference on performance-based fire and fire protection engineering (ICPFFPE 2015)2016;135:12–18.
– reference: Selvek R, Surynek P. Engineering smart behavior in evacuation planning using local cooperative path finding algorithms and agent-based simulations. In: Proceedings of the 11th International Joint Conference on knowledge discovery, knowledge engineering and knowledge management—Volume 2: KEOD, 2019; p. 137–143. INSTICC, SciTePress. https://doi.org/10.5220/0008071501370143.
– reference: Malcolm Ryan. Graph decomposition for efficient multi-robot path planning. In: IJCAI 2007, Proceedings of the 20th International Joint Conference on artificial intelligence, Hyderabad, India, January 6–12, 2007; pp. 2003–2008, 2007.
– reference: Surynek P. A novel approach to path planning for multiple robots in bi-connected graphs. In Proceedings of the 2009 IEEE International Conference on Robotics and Automation, ICRA 2009, Kobe, Japan, May 12–17, IEEE; 2009. pp. 3613–3619
– reference: KamiyamaNKatohNTakizawaAAn efficient algorithm for evacuation problem in dynamic network flows with uniform arc capacityIEICE Trans Inf Syst200689–D82372237910.1093/ietisy/e89-d.8.23721137.90348
– reference: Silver D. Cooperative pathfinding. In: Proceedings of the First Artificial Intelligence and Interactive Digital Entertainment Conference, June 1–5, 2005, Marina del Rey, California, USA: AAAI Press; 2005. pp. 117–122.
– reference: Arbib C, Muccini H, Moghaddam MT. Applying a network flow model to quick and safe evacuation of people from a building: a real case. In: Proceedings of the GEOSAFE Workshop on robust solutions for fire fighting, CEUR Workshop Proceedings 2146 (RSFF 2018), L’Aquila, Italy July 19–20, 2018; pp. 50–61.
– reference: WilenskyURandWAn Introduction to agent-based modeling: modeling natural, social, and engineered complex systems with NetLogo2015CambridgeThe MIT Press
– reference: Chen H, Wang X, Shen L, Cong Y. Formation flight of fixed-wing uav swarms: a group-based hierarchical approach. Chin J Aeronaut. 2020. https://doi.org/10.1016/j.cja.2020.03.006, www.sciencedirect.com/science/article/pii/S1000936120301205. Accessed Oct 2021.
– reference: Standley T. S. Finding optimal solutions to cooperative pathfinding problems. In: Fox M, Poole D. editors. Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2010, Atlanta, Georgia, USA, July 11–15, AAAI Press; 2010. pp. 173–178
– reference: KurdiHAAl-MegrenSAlthunyanRAlmulifiAEffect of exit placement on evacuation plansEur J Oper Res2018269274975910.1016/j.ejor.2018.01.0501388.90124
– reference: TrivediARaoSAgent-based modeling of emergency evacuations considering human panic behaviorIEEE Trans Comput Soc Syst20185127728810.1109/TCSS.2017.2783332
– reference: Zia K, Ferscha A. An agent-based model of crowd evacuation: combining individual, social and technological aspects. In: Proceedings of the 2019 ACM SIGSIM Conference on principles of advanced discrete simulation, SIGSIM-PADS 2020, Miami, FL, USA, June 15–17, ACM; 2020. pp. 129–140.
– reference: Chen L, Tang TQ, Song Z, Huang HJ, Guo RY. Child behavior during evacuation under non-emergency situations: experimental and simulation results 2018. https://www.sciencedirect.com/science/article/abs/pii/S1569190X18301539. Accessed Oct 2021.
– reference: Ng C, Chow W. A brief review on the time line concept in evacuation. 2006. https://www.bse.polyu.edu.hk/researchcentre/fire_engineering/summary_of_output/journal/IJAS/V7/p1-13.pdf. Accessed Oct 2021.
– reference: Zhang J, Li C, Kosov S, Grzegorzek M, Shirahama K, Jiang T, Sun C, Li Z, Li H. Lcu-net: A novel low-cost u-net for environmental microorganism image segmentation. Pattern Recogn. 2021;115. https://doi.org/10.1016/j.patcog.2021.107885, www.sciencedirect.com/science/article/pii/S0031320321000728. Accessed Oct 2021.
– reference: Mas E, Koshimura S, Imamura F, Suppasri A, Muhari A, Adriano B. Recent advances in agent-based tsunami evacuation simulations: case studies in Indonesia, Thailand, Japan and Peru 2015. https://link.springer.com/article/10.1007/s00024-015-1105-y. Accessed Oct 2021.
– reference: Tsurushima A. Reproducing evacuation behaviors of evacuees during the great east japan earthquake using the evacuation decision model with realistic settings. In: Rocha A. P., Steels L, van den Herik H. J.. editors. Proceedings of the 13th International Conference on Agents and Artificial Intelligence, ICAART 2021, Volume 1, Online Streaming, February 4–6, 2021; p. 17–27. SCITEPRESS https://doi.org/10.5220/0010167700170027, https://doi.org/10.5220/0010167700170027. Accessed Oct 2021.
– reference: Even C, Pillac V, Hentenryck PV. NICTA evacuation planner: actionable evacuation plans with contraflows. In: ECAI 2014—21st European Conference on Artificial Intelligence, 18–22 August 2014, Prague, Czech Republic—including prestigious applications of intelligent systems (PAIS 2014). Frontiers in artificial intelligence and applications, 2014;263:1143–48. IOS Press.
– reference: LiuHXuBLuDZhangGA path planning approach for crowd evacuation in buildings based on improved artificial bee colony algorithmAppl Soft Comput20186836037610.1016/j.asoc.2018.04.015
– reference: Bazior G, Palka D, Was J. Cellular automata based modeling of competitive evacuation. In: Cellular automata–13th International Conference on cellular automata for research and industry, ACRI 2018, Como, Italy, September 17–21, 2018, Proceedings. Lecture Notes in Computer Science, vol. 11115, 2018; p. 451–459. Springer.
– reference: ChalmetLGFrancisRLSaundersPBNetwork models for building evacuationFire Technol19821819011310.1007/BF02993491
– reference: Zafar M, Zia K, Muhammad A, Ferscha A. An agent-based model of crowd evacuation integrating agent perception and proximity pressure. In: Proceedings of the 14th International Conference on advances in mobile computing and multi media, MoMM 2016, Singapore, November 28–30, ACM; 2016. pp. 12–19.
– reference: RussellSNorvigPArtificial intelligence: a modern approach20103Pearson Education0835.68093
– reference: Sharon G, Stern R, Felner A, Sturtevant N.R. Conflict-based search for optimal multi-agent pathfinding. Artificial Intelligence, vol. 219. Elsevier; 2014. pp. 40–66.
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  doi: 10.1016/j.proeng.2016.01.072
– ident: 1437_CR20
  doi: 10.1007/978-3-642-31500-8_59
– ident: 1437_CR27
  doi: 10.1145/3007120.3007143
– volume: 18
  start-page: 90
  issue: 1
  year: 1982
  ident: 1437_CR3
  publication-title: Fire Technol
  doi: 10.1007/BF02993491
– volume: 4
  start-page: 100
  issue: 2
  year: 1968
  ident: 1437_CR8
  publication-title: IEEE Trans Syst Sci Cybern SSC
  doi: 10.1109/TSSC.1968.300136
– ident: 1437_CR28
  doi: 10.1016/j.patcog.2021.107885
– ident: 1437_CR17
– ident: 1437_CR15
– ident: 1437_CR18
  doi: 10.5220/0008071501370143
– ident: 1437_CR7
– ident: 1437_CR23
  doi: 10.1109/ROBOT.2009.5152326
– ident: 1437_CR1
– ident: 1437_CR5
– volume: 68
  start-page: 360
  year: 2018
  ident: 1437_CR13
  publication-title: Appl Soft Comput
  doi: 10.1016/j.asoc.2018.04.015
– ident: 1437_CR21
  doi: 10.1609/aiide.v1i1.18726
– ident: 1437_CR9
  doi: 10.5220/0010678200003064
– ident: 1437_CR14
  doi: 10.5220/0005104802490254
– ident: 1437_CR25
  doi: 10.5220/0010167700170027
– volume: 269
  start-page: 749
  issue: 2
  year: 2018
  ident: 1437_CR11
  publication-title: Eur J Oper Res
  doi: 10.1016/j.ejor.2018.01.050
– ident: 1437_CR19
  doi: 10.1016/j.artint.2014.11.006
– volume-title: Artificial intelligence: a modern approach
  year: 2010
  ident: 1437_CR16
– volume: 5
  start-page: 277
  issue: 1
  year: 2018
  ident: 1437_CR24
  publication-title: IEEE Trans Comput Soc Syst
  doi: 10.1109/TCSS.2017.2783332
– ident: 1437_CR4
  doi: 10.1016/j.cja.2020.03.006
– ident: 1437_CR22
  doi: 10.1609/aaai.v24i1.7564
– volume: 89–D
  start-page: 2372
  issue: 8
  year: 2006
  ident: 1437_CR10
  publication-title: IEICE Trans Inf Syst
  doi: 10.1093/ietisy/e89-d.8.2372
– volume-title: An Introduction to agent-based modeling: modeling natural, social, and engineered complex systems with NetLogo
  year: 2015
  ident: 1437_CR26
– ident: 1437_CR2
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Snippet We address the problem of evacuation from the perspective of agent-based modeling (ABM) in this paper. The evacuation problem is modeled as a navigation of...
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SubjectTerms Agent-based models
Algorithms
Apexes
Collision avoidance
Computer Imaging
Computer Science
Computer Systems Organization and Communication Networks
Data Structures and Information Theory
Evacuation
Graph theory
Information Systems and Communication Service
Knowledge Discovery
Knowledge Engineering and Knowledge Management
Modelling
Multiagent systems
Original Research
Pattern Recognition and Graphics
Reagents
School environment
Software Engineering/Programming and Operating Systems
Vision
Title Agent-Based Modeling in Hierarchical Control of Swarms During Evacuation
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