Skip to main content
SpringerLink
Log in

Towards Smart Behavior of Agents in Evacuation Planning Based on Local Cooperative Path Finding

  • Conference paper
  • First Online:
Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1297))

Abstract

We address engineering of smart behavior of agents in evacuation problems from the perspective of cooperative path finding (CPF) in this paper. We introduce an abstract version of evacuation problems we call multi-agent evacuation (MAE) that consists of an undirected graph representing the map of the environment and a set of agents moving in this graph. The task is to move agents from the endangered part of the graph into the safe part as quickly as possible. Although the abstract evacuation task can be solved using centralized algorithms based on network flows that are near-optimal with respect to various objectives, such algorithms would hardly be applicable in practice since real agents will not be able to follow the centrally created plan. Therefore we designed a decentralized evacuation planning algorithm called LC-MAE based on local rules derived from local cooperative path finding (CPF) algorithms. We compared LC-MAE with near-optimal centralized algorithm using agent-based simulations in multiple real-life scenarios. Our finding it that LC-MAE produces solutions that are only worse than the optimum by a small factor. Moreover our approach led to important observations about how many agents need to behave rationally to increase the speed of evacuation. A small fraction of rational agents can speed up the evacuation dramatically.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Alternative definitions of possible movements in CPF exist that for example permit train of agents to move simultaneously atc.

  2. 2.

    Only using half of the planned path before replanning is a simple way of improving agent cooperation described in [22].

References

  1. Arbib, C., Muccini, H., Moghaddam, M.T.: 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, RSFF 2018, L’Aquila, Italy, 19–20 July 2018, pp. 50–61 (2018)

    Google Scholar 

  2. Bompadre, A., Orlin, J.B.: A simple method for improving the primal simplex method for the multicommodity flow problem. Networks 51(1), 63–77 (2008)

    Article  MathSciNet  Google Scholar 

  3. Cacchiani, V., Jünger, M., Liers, F., Lodi, A., Schmidt, D.R.: Single-commodity robust network design with finite and hose demand sets. Math. Program. 157(1), 297–342 (2016)

    Article  MathSciNet  Google Scholar 

  4. Chalmet, L.G., Francis, R.L., Saunders, P.B.: Network models for building evacuation. Fire Technol. 18(1), 90–113 (1982)

    Article  Google Scholar 

  5. Craven, P.V.: The python arcade library, March 2019. http://arcade.academy

  6. Felner, A., et al.: Adding heuristics to conflict-based search for multi-agent path finding. In: Proceedings of the Twenty-Eighth International Conference on Automated Planning and Scheduling, ICAPS 2018, Delft, The Netherlands, 24–29 June 2018, pp. 83–87 (2018)

    Google Scholar 

  7. Foudil, C., Djedi, N., Sanza, C., Duthen, Y.: Path finding and collision avoidance in crowd simulation. CIT 17, 217–228 (2009)

    Article  Google Scholar 

  8. Ghallab, M., Nau, D.S., Traverso, P.: Automated Planning and Acting. Cambridge University Press (2016). http://www.cambridge.org/de/academic/subjects/computer-science/artificial-intelligence-and-natural-language-processing/automated-planning-and-acting?format=HB

  9. Goldberg, A.V., Tarjan, R.E.: A new approach to the maximum-flow problem. J. ACM 35(4), 921–940 (1988). https://doi.org/10.1145/48014.61051

    Article  MathSciNet  MATH  Google Scholar 

  10. Hagberg, A.A., Schult, D.A., Swart, P.J.: Exploring network structure, dynamics, and function using networkx. In: Proceedings of the 7th Python in Science Conference, pp. 11–15 (2008)

    Google Scholar 

  11. Hudziak, M., Pozniak-Koszalka, I., Koszalka, L., Kasprzak, A.: Comparison of algorithms for multi-agent pathfinding in crowded environment. In: Nguyen, N.T., Trawiński, B., Kosala, R. (eds.) ACIIDS 2015. LNCS (LNAI), vol. 9011, pp. 229–238. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-15702-3_23

    Chapter  Google Scholar 

  12. Korf, R.E., Taylor, L.A.: Finding optimal solutions to the twenty-four puzzle. In: Proceedings of the Thirteenth National Conference on Artificial Intelligence and Eighth Innovative Applications of Artificial Intelligence Conference, AAAI 1996, IAAI 1996, Portland, Oregon, USA, 4–8 August 1996, vol. 2, pp. 1202–1207 (1996)

    Google Scholar 

  13. Kurdi, H.A., Al-Megren, S., Althunyan, R., Almulifi, A.: Effect of exit placement on evacuation plans. Eur. J. Oper. Res. 269(2), 749–759 (2018)

    Article  Google Scholar 

  14. Liu, C., li Mao, Z., min Fu, Z.: Emergency evacuation model and algorithm in the building with several exits. Procedia Eng. 135, 12–18 (2016). 2015 International Conference on Performance-based Fire and Fire Protection Engineering (ICPFFPE 2015)

    Google Scholar 

  15. Liu, Z., Wu, B., Lin, H.: Coordinated robot-assisted human crowd evacuation. In: 57th IEEE Conference on Decision and Control, CDC 2018, Miami, FL, USA, 17–19 December 2018, pp. 4481–4486 (2018)

    Google Scholar 

  16. Mishra, G., Mazumdar, S., Pal, A.: Improved algorithms for the evacuation route planning problem. In: Lu, Z., Kim, D., Wu, W., Li, W., Du, D.-Z. (eds.) COCOA 2015. LNCS, vol. 9486, pp. 3–19. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-26626-8_1

    Chapter  Google Scholar 

  17. Sánchez-Oro, J., Duarte, A.: An experimental comparison of variable neighborhood search variants for the minimization of the vertex-cut in layout problems. Electron. Not. Discrete Math. 39, 59–66 (2012)

    Article  MathSciNet  Google Scholar 

  18. 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, IC3K 2019, Volume 2: KEOD, Vienna, Austria, 17–19 September 2019, pp. 137–143 (2019)

    Google Scholar 

  19. Sharma, S.: AVATARSIM: a multi-agent system for emergency evacuation simulation. In: 17th International Conference on Software Engineering and Data Engineering (SEDE-2008), 30 June–2 July 2008, Omni Los Angeles Hotel at California Plaza, Los Angeles, California, USA, Proceedings. pp. 163–167 (2008)

    Google Scholar 

  20. Shekhar, S., et al.: Experiences with evacuation route planning algorithms. Int. J. Geogr. Inf. Sci. 26(12), 2253–2265 (2012)

    Article  Google Scholar 

  21. Silver, D.: Cooperative pathfinding. In: Young, R.M., Laird, J.E. (eds.) Proceedings of the First Artificial Intelligence and Interactive Digital Entertainment Conference, 1–5 June 2005, Marina del Rey, California, USA, pp. 117–122. AAAI Press (2005)

    Google Scholar 

  22. Silver, D.: Cooperative pathfinding. In: AI Game Programming Wisdom, vol. 3 (2006)

    Google Scholar 

  23. Surynek, P.: Reduced time-expansion graphs and goal decomposition for solving cooperative path finding sub-optimally. In: IJCAI, pp. 1916–1922 (2015)

    Google Scholar 

  24. Surynek, P.: Solving abstract cooperative path-finding in densely populated environments. Comput. Intell. 30(2), 402–450 (2014)

    Article  MathSciNet  Google Scholar 

  25. Wang, K., Botea, A.: MAPP: a scalable multi-agent path planning algorithm with tractability and completeness guarantees. JAIR 42, 55–90 (2011)

    MathSciNet  MATH  Google Scholar 

  26. Yu, J., LaValle, S.M.: Multi-agent path planning and network flow. In: Algorithmic Foundations of Robotics X - Proceedings of the Tenth Workshop on the Algorithmic Foundations of Robotics, WAFR 2012, pp. 157–173 (2012)

    Google Scholar 

  27. Yu, J., LaValle, S.M.: Optimal multi-robot path planning on graphs: structure and computational complexity. CoRR abs/1507.03289 (2015). http://arxiv.org/abs/1507.03289

Download references

Acknowledgements

This research has been supported by GAČR - the Czech Science Foundation, grant registration number 19-17966S. We would like to thank anonymous reviewers for their valuable comments.

Author information

Authors and Affiliations

  1. Faculty of Information Technology, Czech Technical University in Prague, Thákurova 9, 160 00, Prague, Czech Republic

    Róbert Selvek & Pavel Surynek

Authors
  1. Róbert Selvek
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pavel Surynek
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pavel Surynek .

Editor information

Editors and Affiliations

  1. Instituto de Telecomunicações, Lisbon, Portugal

    Ana Fred

  2. Federal University of Pernambuco, Recife, Brazil

    Ana Salgado

  3. University of Madeira, Funchal, Portugal

    David Aveiro

  4. Delft University of Technology, Delft, The Netherlands

    Jan Dietz

  5. Polytechnic Institute of Coimbra, Coimbra, Portugal

    Jorge Bernardino

  6. Polytechnic Institute of Setúbal, Setúbal, Portugal

    Joaquim Filipe

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Selvek, R., Surynek, P. (2020). Towards Smart Behavior of Agents in Evacuation Planning Based on Local Cooperative Path Finding. In: Fred, A., Salgado, A., Aveiro, D., Dietz, J., Bernardino, J., Filipe, J. (eds) Knowledge Discovery, Knowledge Engineering and Knowledge Management. IC3K 2019. Communications in Computer and Information Science, vol 1297. Springer, Cham. https://doi.org/10.1007/978-3-030-66196-0_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-66196-0_14

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-66195-3

  • Online ISBN: 978-3-030-66196-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation