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Towards addressing dynamic multi-agent task allocation in law enforcement

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Abstract

Police officers conduct routine patrols and perform tasks in response to reported incidents. The importance of each task varies from low (e.g. noise complaint) to high (e.g. murder). The workload associated with each task, indicating the amount of work to be completed for the incident to be processed, may vary as well. Multiple officers with heterogeneous skills may work together on important tasks to share the workload and improve response time. To deal with the underlying law enforcement problem (LEPH), one needs to allocate police officers to dynamic tasks whose locations, arrival times, and importance levels are unknown a priori. Addressing this challenge and inspired by real police logs, this research aims to solve the LEPH problem by using and comparing three methods: Fisher market-based FMC_TAH+, swarm intelligence HDBA, and Simulated Annealing SA algorithms. FMC_TAH+ is implemented, using agents as buyers and tasks as goods, to compute fair allocations (i.e. envy-free), and efficient (i.e. Pareto-optimal) in a polynomial or pseudo-polynomial time. FMC_TAH+ allocations are heuristically scheduled, considering inter-agent constraints on shared tasks. HDBA, a probabilistic swarm intelligence algorithm inspired by the emergent behavior of social bees, was previously implemented to allocate agents to tasks based on agent performance, task priorities, and distances between agents and task-execution locations. SA is a meta-heuristic for approximating the global optimums in large optimization problems. The three methods were compared in this study for five different performance measures that are commonly used by law enforcement authorities. The results indicate an advantage for FMC_TAH+ both in total utility and in the average arrival time to tasks. Also, compared respectively to HDBA and SA, FMC_TAH+ leads to 34% and 32% higher team utility in the highest shift workload.

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References

  1. Agmon, N., Kaminka, G. A., Kraus, S., & Traub, M. (2010). Task reallocation in multi-robot formations. Journal of Physical Agents, 4(2), 1–10.

    Google Scholar 

  2. Amador, S., Okamoto, S., & Zivan, R. (2014). Dynamic multi-agent task allocation with spatial and temporal constraints. In Proceedings of the 2014 international conference on autonomous agents and multiagent systems (AAMAS) (pp. 1495–1496).

  3. Amador, S., & Zivan, R. (2017). Incentivizing cooperation between heterogeneous agents in dynamic task allocation. In Proceedings 16th international conference on autonomous agents and multiagent systems (AAMAS) (pp. 1082–1090).

  4. Attiya, G., & Hamam, Y. (2006). Task allocation for maximizing reliability of distributed systems: a simulated annealing approach. Journal of parallel and Distributed Computing, 66(10), 1259–1266.

    Article  Google Scholar 

  5. Bayındır, L. (2016). A review of swarm robotics tasks. Neurocomputing, 172, 292–321.

    Article  Google Scholar 

  6. Berman, S., Halasz, A., Kumar, V., & Pratt, S. (2007). Bio-inspired group behaviors for the deployment of a swarm of robots to multiple destinations. In Proceedings of the IEEE international conference on robotics and automation (pp. 2318–2323).

  7. Bonabeau, E., Dorigo, M., & Theraulaz, G. (1999). Swarm intelligence: From natural to artificial systems. Press Inc, New York, NY, USA: Oxford Univ.

    Book  Google Scholar 

  8. Cao, J., Yin, B., Lu, X., Kang, Y., & Chen, X. (2017). A modified artificial bee colony approach for the 0–1 knapsack problem. Applied Intelligence, 48, 1582–1595.

    Article  Google Scholar 

  9. Chapman, A. C., Micillo, R. A., Kota, R., & Jennings, N. R. (2010). Decentralized dynamic task allocation using overlapping potential games. The Computer Journal, 53(9), 1462–1477.

    Article  Google Scholar 

  10. Colby, M., Chung, J. J., & Tumer, K. (2015). Implicit adaptive multi-robot coordination in dynamic environments. In IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 5168–5173), Hamburg, Germany.

  11. Devanur, N. R., Papadimitriou, C. H., Saberi, A., & Vazirani, V. V. (2002). Market equilibrium via a primal-dual-type algorithm. In Proceedings of the 43rd Symposium on Foundations of Computer Science (pp. 389–395), Washington, DC, USA.

  12. Fateh, B., & Govindarasu, M. (2015). Joint scheduling of tasks and messages for energy minimization in interference-aware real-time sensor networks. IEEE Transactions on Mobile Computing, 14(1), 86–98.

    Article  Google Scholar 

  13. Fitzgerald, J., & Griffin, G. (2017). Pareto optimal decision making in a distributed opportunistic sensing problem. IEEE Transactions on Cybernetics, 99, 1–7.

    Google Scholar 

  14. Fu, B., Liang, Y., & Chen, C. (2015). Bio-inspired group modeling and analysis for intruder detection in mobile sensor/robotic networks. IEEE Transactions on Cybernetics, 45(1), 103–115.

    Article  Google Scholar 

  15. Gale, D. (1960). The Theory of Linear Economic Models. New York: McGraw-Hill.

    MATH  Google Scholar 

  16. Gerkey, B. P., & Matarić, M. J. (2002). Sold!: Auction methods for multirobot coordination. IEEE Transactions on Robotics and Automation, 18(5), 758–768.

    Article  Google Scholar 

  17. Gerkey, B. P., & Matarić, M. J. (2003). Multi-robot task allocation: Analyzing the complexity and optimality of key architectures. IEEE International Conference on Robotics and Automation, 3, 3862–3868.

    Google Scholar 

  18. Ghaffarinasab, N., Motallebzadeh, A., Jabarzadeh, Y., & Kara, B. Y. (2018). Efficient simulated annealing based solution approaches to the competitive single and multiple allocation hub location problems. Computers & Operations Research, 90, 173–192.

    Article  MathSciNet  Google Scholar 

  19. Groβ, R., Nouyan, S., Bonani, M., Mondada, F., & Dorigo, M. (2008). Division of labor in self-organized groups. In Proceedings of the 10th international conference on simulation of adaptive behavior: from animals to animats (pp. 426–436). Springer-Verlag, Berlin.

  20. Guerrero, J., & Oliver, G. (2012). Multi-robot coalition formation in real-time scenarios. Robotics and Autonomous Systems, 60, 1295–1307.

    Article  Google Scholar 

  21. Hu, W., Yu, Y., & Gu, W. (2018). Parameter estimation of fractional-order arbitrary dimensional hyperchaotic systems via a hybrid adaptive artificial bee colony algorithm with simulated annealing algorithm. Engineering Applications of Artificial Intelligence, 68, 172–191.

    Article  Google Scholar 

  22. Jevtić, A., Gutierrez, A., Andina, D., & Jamshidi, M. (2012). Distributed bees algorithm for task allocation in swarm of robots. IEEE Systems Journal, 6(2), 296–304.

    Article  Google Scholar 

  23. Jones, E. G., Dias, M. B., & Stentz, A. (2007). Learning-enhanced market-based task allocation for oversubscribed domains. In Proceedings of the IEEE/RSJ international conference on intelligent robots and systems, San Diego, CA.

  24. Labella, T. H., Dorigo, M., & Deneubourg, J. L. (2006). Division of labor in a group of robots inspired by ants’ foraging behavior. ACM Transactions on Autonomous Adaptive Systems, 1(1), 4–25.

    Article  Google Scholar 

  25. Lau, H. C., & Zhang, L. (2003). Task allocation via multi-agent coalition formation: Taxonomy, algorithms and complexity. In Proceedings of the 15th IEEE international conference on tools with artificial intelligence (pp. 346–350), Sacramento, CA, USA.

  26. Matarić, M. J., Sukhatme, G. S., & Âstergaard, E. H. (2003). Multi-robot task allocation in uncertain environments. Autonomous Robots, 14(2), 255–263.

    Article  Google Scholar 

  27. Nanjanath, M., & Gini, M. (2010). Repeated auctions for robust task execution by a robot team. Robotics and Autonomous Systems, 58(7), 900–909.

    Article  Google Scholar 

  28. Ramchurn, S. D., Farinelli, A., Macarthur, K. S., & Jennings, N. R. (2010). Decentralized Coordination in robo cup rescue. The Computer Journal, 53(9), 1447–1461.

    Article  Google Scholar 

  29. Reeves, C. R. (1993). Modern heuristic techniques for combinatorial problems. New York: Wiley.

    MATH  Google Scholar 

  30. Reijnierse, J. H., & Potters, J. A. M. (1998). On finding an envy-free Pareto-optimal division. Mathematical Programming, 83, 291–311.

    MathSciNet  MATH  Google Scholar 

  31. Schoneveld, A., de Ronde, J. F., & Sloot, P. M. A. (1997). On the Complexity of Task Allocation. Journal of Complexity, 3, 52–60.

    Article  MathSciNet  Google Scholar 

  32. Skiena, S. S. (1998). The algorithm design manual: text (Vol. 1). Berlin: Springer.

    MATH  Google Scholar 

  33. Tan, Y., & Ding, K. (2016). Survey of GPU-based implementation of swarm intelligence algorithms. IEEE Transactions on Cybernetics, 46(9), 2028–2041.

    Article  Google Scholar 

  34. Tang, Y. (2016). Coordination of multi-agent systems under switching topologies via disturbance observer-based approach. International Journal of Systems Science, 47, 3900–3907.

    Article  MathSciNet  Google Scholar 

  35. Timotheou, S., & Loukas, G. (2009). Autonomous networked robots for the establishment of wireless communication in uncertain emergency response scenarios. In Proceedings of the ACM symposium on applied computing (pp. 171–1175).

  36. Tkach, I., Edan, Y., & Nof, S. Y. (2017). Multi-sensor task allocation framework for supply networks security using task administration protocols. International Journal of Production Research, 55, 5202–5224.

    Article  Google Scholar 

  37. Tkach, I., Jevtić, A., Edan, Y., & Nof, S. Y. (2018). A Modified Distributed Bees Algorithm for Multi-Sensor Task Allocation. Sensors, 18(3), 759. https://doi.org/10.3390/s18030759.

    Article  Google Scholar 

  38. Tkach, I., & Edan, Y. (2020). Extended examples of single-layer multi-sensor systems. In I. Tkach & Y. Edan (Eds.), Distributed heterogeneous multi sensor task. Automation, collaboration, & e-services (Vol. 7). Cham: Springer.

    Chapter  Google Scholar 

  39. Wei, L., Zhang, Z., Zhang, D., & Leung, S. C. (2018). A simulated annealing algorithm for the capacitated vehicle routing problem with two-dimensional loading constraints. European Journal of Operational Research, 265(3), 843–859.

    Article  MathSciNet  Google Scholar 

  40. Wolski, R., Plank, J. S., Brevik, J., & Bryan, T. (2001). Analyzing market-based resource allocation strategies for the computational grid. International Journal of High Performance Computing Applications, 15(3), 258–281.

    Article  Google Scholar 

  41. Xiong, N., & Svensson, P. (2002). Multi-sensor management for sensor fusion: Issues and approaches. Information Fusion, 3, 163–186.

    Article  Google Scholar 

  42. Yuan, Q., Guan, Y., Hong, B., & Meng, X. (2013). Multi-robot task allocation using CNP combines with neural network. Neural Computing and Applications, 23(7–8), 1909–1914.

    Article  Google Scholar 

  43. Zhang, L. (2011). Proportional response dynamics in the Fisher market. Theoretical Computer Science, 412(24), 2691–2698.

    Article  MathSciNet  Google Scholar 

  44. Zhang, S. Z., & Lee, C. K. M. (2015). An improved artificial bee colony algorithm for the capacitated vehicle routing problem. In: IEEE interantional conference on systems, man, and cybernetics (SMC) (pp. 2124–2128), Hong Kong.

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Acknowledgments

The contribution of Professor Emeritus Nava Pliskin to the manuscript is acknowledged with gratitude.

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Authors and Affiliations

  1. Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Itshak Tkach

  2. Faculty of Technology Management, Holon Institute of Technology, Holon, Israel

    Sofia Amador

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  1. Itshak Tkach
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  2. Sofia Amador
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Correspondence to Itshak Tkach.

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Tkach, I., Amador, S. Towards addressing dynamic multi-agent task allocation in law enforcement. Auton Agent Multi-Agent Syst 35, 11 (2021). https://doi.org/10.1007/s10458-021-09494-x

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