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 in | SN computer science Vol. 4; no. 1; p. 38 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Singapore
Springer Nature Singapore
01.11.2022
Springer Nature B.V |
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| Online Access | Get full text |
| ISSN | 2661-8907 2662-995X 2661-8907 |
| DOI | 10.1007/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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| Copyright | The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Keywords | Multi-agent pathfinding ABM Evacuation Conflict-based search Hierarchical control of swarms |
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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. 1437_CR23 1437_CR25 1437_CR28 1437_CR27 A Trivedi (1437_CR24) 2018; 5 1437_CR29 H Liu (1437_CR13) 2018; 68 1437_CR20 1437_CR22 1437_CR21 PE Hart (1437_CR8) 1968; 4 1437_CR12 1437_CR15 1437_CR14 1437_CR17 1437_CR19 1437_CR18 1437_CR7 1437_CR9 U Wilensky (1437_CR26) 2015 S Russell (1437_CR16) 2010 1437_CR1 1437_CR2 LG Chalmet (1437_CR3) 1982; 18 1437_CR4 1437_CR5 1437_CR6 HA Kurdi (1437_CR11) 2018; 269 N Kamiyama (1437_CR10) 2006; 89–D |
| 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. 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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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