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Distributed task allocation in multi-agent environments using cellular learning automata

  • Maryam Khani
  • Ali Ahmadi
  • Hajar Hajary
Methodologies and Application

Abstract

People’s safety is threatened by the repetition of critical events. Many people lose their lives due to unprofessional rescue operation as well as time pressure of the rescue operation. A key problem in urban search and rescue teams, considering the severe turbulence and complexity of the environments which are hit by a crisis, is the coordination between the team members. In order to solve this problem, an effective plan would be the provision of measures where human works with intelligent assistant agents to assign the tasks in any way. Dynamic tasks are identified by the human agent of the rescue team in the crisis environment and are characterized by spatial–temporal characteristics assigned to the appropriate rescue team by the intelligent assistant agents who apply intelligent decision-making techniques. The objective of this study is to propose a new approach for allocating spatial–temporal tasks in multi-agent systems through cellular learning automata as the decision-making technique. Results obtained here indicate that this proposed model can significantly improve the rescue time and space. Rescue teams could cover all critical areas by going through the minimum distance to make maximum use of time.

Keywords

Spatial–temporal task allocation Multi-agent system Cellular learning automata Earthquake emergency response Geospatial simulation Urban search and rescue 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

References

  1. Adams NM, Field M, Gelenbe E, Hand DJ et al (2008) The Aladdin project: intelligent agents for disaster management. In: IARP/EURON workshop on robotics for risky interventions and environmental surveillanceGoogle Scholar
  2. Akbari Torkestani J (2012) An adaptive learning automata-based ranking function discovery algorithm. J Intell Inf Syst 39:441–459CrossRefGoogle Scholar
  3. Ashish N, Eguchi R, Hegde R et al (2007) Situational awareness technologies for disaster response. In: Chen H et al (ed) Terrorism informatics: knowledge management and data mining for homeland security. Springer, Boston, MA, pp 517–544Google Scholar
  4. Barzegar S, Davoudpour M, Meybodi MR et al (2011) Formalized learning automata with adaptive fuzzy coloured Petri net; an application specific to managing traffic signals. Sci Iran 18(3):554–565CrossRefGoogle Scholar
  5. Beigy H, Meybodi MR (2003) A self-organizing channel assignment algorithm: a cellular learning automata approach. Lecture notes in computer science. Springer, Berlin, pp 119–126Google Scholar
  6. Beigy H, Meybodi MR (2004) A mathematical framework for cellular learning automata. Adv Complex Syst. doi: 10.1142/S0219525904000202 MathSciNetMATHGoogle Scholar
  7. Beigy H, Meybodi MR (2008) Asynchronous cellular learning automata. Automatica 44(5):1350–1357MathSciNetCrossRefMATHGoogle Scholar
  8. Brutschy A, Pini G (2014) Self-organized task allocation to sequentially interdependent tasks in swarm robotics. Auton Agents Multi Agent Syst 28(1):101–125CrossRefGoogle Scholar
  9. Campo A, Dorigo M (2007) Efficient multi-foraging in swarm robotics. In: Advances in artificial life, lecture notes in computer science, vol 4648, pp 696–705Google Scholar
  10. Castelloh E, Yamamoto T, Nakamura Y, Ishiguro H (2013) Task allocation for a robotic swarm based on an adaptive response threshold model. In: 13th International conference on control, automation and systems. Kimdaejung Convention Center, Gwangju, Korea, pp 259–266Google Scholar
  11. Cayrpunar O, Tavl B, Gazi V (2008) Dynamic robot networks for search and rescue operations. In: International workshop on robotics for risky interventions and surveillance of the environmentGoogle Scholar
  12. Cornejo A, Dornhaus A et al (2014) Task allocation in ant colonies. Distrib Comput 8784:46–60MathSciNetGoogle Scholar
  13. Crooks AT, Castle CJE (2012) The integration of agent-based modelling and geographical information for geospatial simulation. In: Agent-based models of geographical systems. Springer, Dordrecht, pp 219–251Google Scholar
  14. Dias B (2004) Traderbots: a new paradigm for robust and efficient multirobot coordination in dynamics environments. Ph.D. dissertation, Robotics Institute, Carnegie Mellon University, PittsburghGoogle Scholar
  15. Dias B, Stentz A (2000) A free market architecture for distributed control of a multirobot system. In: 6th International conference on intelligent autonomous systems, pp 115–122Google Scholar
  16. Dias MB, Zlot R, Kalra N, Stentz A (2006) Market-based multirobot coordination: a survey and analysis. Proc IEEE 94:1257–1270CrossRefGoogle Scholar
  17. Dorigo M (2005) SWARM-BOT: an experiment in swarm robotics. In: Arabshahi P, Martinoli A (eds) 2005 IEEE swarm intelligence symposium, pp 192–200Google Scholar
  18. Dorigo M, Floreano D, Gambardella LM, Mondada F, Nolfi S, Baaboura T et al (2013) A novel concept for the study of heterogeneous robotic swarms. IEEE Robot Autom Mag 20(4):60–71CrossRefGoogle Scholar
  19. Dos Santos F, Bazzan ALC (2011) Towards efficient multiagent task allocation in the robocup rescue a biologically-inspired approach. Auton Agents Multi Agent Syst 22:465–486CrossRefGoogle Scholar
  20. Esnaashari M, Meybodi MR (2008) A cellular learning automata based clustering algorithm for wireless sensor networks. Sens Lett 6:723–735CrossRefMATHGoogle Scholar
  21. Esnaashari M, Meybodi MR (2010) Dynamic point coverage problem in wireless sensor networks: a cellular learning automata approach. Ad Hoc Sens Wirel Netw 10:193–234MATHGoogle Scholar
  22. Fasli M, Michalakopoulos M (2006) Developing software agents using.NET. University of Essex Department of Computer Science, ColchesterGoogle Scholar
  23. Fathy Navid AH, Aghababa AB (2013) Cellular learning automata and its applications. In: Computer and information science, numerical analysis and scientific computing. Publish with INTECH. doi: 10.5772/52953
  24. Fayyoumi E, Oommen BJ (2009) Achieving micro aggregation for secure statistical databases using fixed structure partitioning based learning automata. IEEE Trans Syst Man Cybern B Cybern 39(5):1192–1205CrossRefGoogle Scholar
  25. Ferreira PR, Boffo FS, Bazzan ALC (2008) Using swarm-GAP for distributed task allocation in complex scenarios. Massively multi-agent technology, LNCS, vol 5043, pp 107–121Google Scholar
  26. Ferreira PR Jr, Dos Santos F, Bazzan ALC, Epstein D, Waskow SJ (2009) RoboCup rescue as multi agent task allocation among teams: experiments with task interdependencies. Auton Agents Multi Agent Syst 20(3):421–443CrossRefGoogle Scholar
  27. Gerkey BP, Mataric MJ (2003) Multi-robot task allocation: analyzing the complexity and optimality of key architectures. In: IEEE international conference on robotics and automation. doi: 10.1109/ROBOT.2003.1242189
  28. Gerkey BP, Mataric MJ (2004) A formal analysis and taxonomy of task allocation in multi-robot systems. Int J Robot Res 23(9):939–954CrossRefGoogle Scholar
  29. Godoy J, Gini M (2013) Task allocation for spatially and temporally distributed tasks. In: Proceedings of the 12th international conference IAS-12, AISC 194. Springer, Berlin, Heidelberg, pp 603–612Google Scholar
  30. Grinshpoun T, Grubshtein A et al (2013) Asymmetric distributed constraint optimization problems. J Artif Intell Res 47:613–647MathSciNetMATHGoogle Scholar
  31. Ham M, Agha G (2007) Market-based coordination strategies for large-scale multi-agent systems. Syst Inf Sci Notes 2(1):126–131Google Scholar
  32. Ham M, Agha G (2008) A study of coordinated dynamic market-based task assignment in massively multi-agent systems. In: Massively multiagent technology, lecture notes in computer science, vol 5043, pp 43–63Google Scholar
  33. Hunsberger L, Grosz BJ (2000) A combinatorial auction for collaborative planning. In: Proceedings of the fourth international conference on multiagent systems, ICMAS, pp 151–158Google Scholar
  34. Hussein A, Khamis A (2013) Market-based approach to multi-robot task allocation. In: International conference on individual and collective behaviors in robotics, pp 69–74Google Scholar
  35. Ikemoto Y, Miura T, Asama H (2010) Adaptive division-of-labor control algorithm for multi-robot systems. J Robot Mechatron 22(4):514–525CrossRefGoogle Scholar
  36. Jain S, McLean CR (2004) An architecture for modeling and simulation of emergency response. In: Proceedings of the 2004 IIE conferenceGoogle Scholar
  37. Jinguo L, Yuechao W, Bin L, Shugen M (2007) Current research, key performances and future development of search and rescue robots. Front Mech Eng China 2:404–416CrossRefGoogle Scholar
  38. Kalra N, Martinoli A (2006) Comparative study of market-based and threshold-based task allocation. Distrib Auton Robot Syst 7:91–101MATHGoogle Scholar
  39. Khalil KM, Abdel-Aziz MH, Nazmy MT, Salem AM (2008) The role of artificial intelligence technologies in crisis response. In: Mendel conference, 14th international conference on soft computingGoogle Scholar
  40. Khalil KM, Abdel-Aziz MH, Nazmy MT, Salem AM (2009) Multi-agent crisis response systems—design requirements and analysis of current systems. In: Fourth international conference on intelligent computing and information systems. Egypt, Cairo, pp 920–925Google Scholar
  41. Koes M, Nourbakhsh I, Sycara K (2005) Heterogeneo multirobot coordination with spatial and temporal constraints. In: Proceedings of the twentieth national conference on artificial intelligence (AAAI), pp 1292–1297Google Scholar
  42. Labella TH, Dorigo M, Deneubourg JL (2006) Division of labor in a group of robots inspired by ants’ foraging behavior. ACM Trans Auton Adapt Syst 1(1):4–25CrossRefGoogle Scholar
  43. Lakshmivarahan S (1981) Learning algorithms: theory and applications. Springer, BerlinCrossRefMATHGoogle Scholar
  44. Law M, Collins A (2013) Getting to know ArcGIS for desktop. Environmental Systems Research Institute, Redlands, pp 19–30Google Scholar
  45. Leite AR, Enembreck F, Barthès JA (2014) Distributed constraint optimization problems: review and perspectives. Expert Syst Appl 41:5139–5157CrossRefGoogle Scholar
  46. Liekna A, Lavendelis E, Grabovskis A (2012) Experimental analysis of contract net protocol in multi-robot task allocation. Appl Comput Syst 13(1):6–14Google Scholar
  47. Liu W, Winfield A, Sa J, Chen J, Dou L (2007) Towards energy optimization: emergent task allocation in a swarm of foraging robots. Adapt Behav 15(3):289–305CrossRefGoogle Scholar
  48. Maheswaran RT, Rogers CM, Sanchez R, Szekely P (2010) Human-agent collaborative optimization of real-time distributed dynamic multi-agent coordination. In: 9th International conference on autonomous agents and multi-agent systems. Canada, Toronto, pp 49–56Google Scholar
  49. Massaguer D, Balasubramanian V, Mehrotra S, Venkatasubramanian N (2006) MultiAgent simulation of disaster response. In: ATDM workshop in AAMAS, JapanGoogle Scholar
  50. Meybodi MR, Kharazmi MR (2004) Application of cellular learning automata to image processing. J Amirkabir 14(56):1101–1126Google Scholar
  51. Misra S, Oommen BJ (2009) Using pursuit automata for estimating stable shortest paths in stochastic network environments. Int J Commun Syst 22(4):441–468CrossRefGoogle Scholar
  52. Misra S, Abraham KI, Obaidat MS, Krishna PV (2009) LAID: a learning automata-based scheme for intrusion detection in wireless sensor networks. Secur Commun Netw 2(2):105–115CrossRefGoogle Scholar
  53. Moghiss V, Meybodi MR, Esnaashari M (2010) An intelligent protocol to channel assignment in wireless sensor networks: learning automata approach. In: International conference on information networking and automation, pp V1-338–V1-343Google Scholar
  54. Murakami Y, Minami K, Kawasoe T, Ishida T (2002) Multi-agent simulation for crisis management. In: IEEE workshop on knowledge media networkingGoogle Scholar
  55. Najim K, Poznyak AS (1994) Learning automata: theory and applications. Pergamon, OxfordMATHGoogle Scholar
  56. Narendra KS, Thathachar MAL (1989) Learning automata: an introduction. Prentice-Hall, Englewood CliffsMATHGoogle Scholar
  57. Neumann JV (1996) The theory of self-reproducing automata. In: Burks AW (ed) The theory of self-reproducing automata. University of Illinois Press, Urbana, LondonGoogle Scholar
  58. Nourjou R, Hatayama M (2011) Intelligent GIS for spatial cooperation of earthquake emergency response. Ann Disaster Prev Res Inst Kyoto Univ 54(B):29–34Google Scholar
  59. Nourjou R, Hatayama M, Tatano H (2011) Introduction to spatially distributed intelligent assistant agents for coordination of human-agent teams’ actions. In: Proceedings of the 2011 IEEE international symposium on safety, security and rescue robotics, Kyoto, Japan, pp 251–258Google Scholar
  60. Nourjou R, Smith SF, Hatayama M, Szekely P (2014a) Intelligent algorithm for assignment of agents to human strategy in centralized multi-agent coordination. J Softw 9(10):2586–2597CrossRefGoogle Scholar
  61. Nourjou R, Szekely P, Hatayama M, Ghafory-Ashtiany M, Smith SF (2014b) Data model of the strategic action planning and scheduling problem in a disaster response team. J Disaster Res 9(3):381–399CrossRefGoogle Scholar
  62. Nourjou R, Hatayama M, Smith SF, Sadeghi A, Szekely P (2014c) Design of a GIS-based assistant software agent for the incident commander to coordinate emergency response operations. In Workshop on robots and sensors integration in future rescue information systemGoogle Scholar
  63. Parker LE (2008) Multiple mobile robot systems. In: Springer handbook of robotics. Springer, pp 921–941Google Scholar
  64. Parks A (2011) Using the open source DotSpatial GIS library to create tasks for the data for environmental modeling (D4EM) system. Research Triangle Institute International, North CarolinaGoogle Scholar
  65. Quiñonez Y, De lope J, Maravall D (2011a) Bio-inspired decentralized self-coordination algorithms. In: 4th International work-conference on the interplay between natural and artificial computation, IWINAC, La Palma, Canary Islands, Spain. Springer, Berlin, Heidelberg, pp 30–39Google Scholar
  66. Quiñonez Y, Maravall D, De lope J (2011b) Stochastic learning automata for self-coordination in heterogeneous multi-tasks selection in multi-robot systems. In: 10th Mexican international conference on artificial intelligence, MICAI 2011, Puebla, Mexico. Springer, Berlin, Heidelberg, pp 443–453Google Scholar
  67. Rahwan T, Jennings NR (2007) An algorithm for distributing coalitional value calculations among cooperating agents. Artif Intell J 171:535–567MathSciNetCrossRefMATHGoogle Scholar
  68. Rasekh A, Vafaeinezhad AR (2012) Developing a GIS based decision support system for resource allocation in earthquake search and rescue operation. In: ICCSA 2012, part II, LNCS 7334. Springer, Berlin, Heidelberg, pp 275–285Google Scholar
  69. Russell S, Norvig P (1995) Artficial intelligence: a modern approach. Prentice-Hall, Saddle RiverMATHGoogle Scholar
  70. Scerri P, Farinelli A, Okamoto S, Tambe M (2005) Allocating tasks in extreme teams. In: Proceedings of the fourth international joint conference on autonomous agents and multiagent systems, pp 727–734Google Scholar
  71. Schiff JL (2008) Cellular learning automata: a discrete view of the world. Wiley, ChichesterMATHGoogle Scholar
  72. Schoenharl T, Madey G et al (2006) WIPER: a multi-agent system for emergency response. In: Proceedings of the 3rd international ISCRAM conference, Newark, NJ, USAGoogle Scholar
  73. Schurr N, Marecki J, Lewis JP, Tambe M, Scerri P (2009) The DEFACTO system: coordinating human-agent teams for the future of disaster response. Published Articles of CREATE Research ArchiveGoogle Scholar
  74. Shiroma P, Campos M (2009) CoMutaR: a framework for multi-robot coordination and task allocation. In: IEEE/RSJ international conference on intelligent robots and systems, pp 4817–4824Google Scholar
  75. Smith RG (1980) The contract net protocol: high-level communication and control in a distributed problem solver. IEEE Trans Comput C–29(12):1104–1113CrossRefGoogle Scholar
  76. Song T, Yan X, Liang A, Chen K, Guan H (2009) A distributed bidirectional auction algorithm for multirobot coordination. In: IEEE international conference on research challenges in computer science, pp 145–148Google Scholar
  77. Suarez Baron SA (2010) Dynamic task allocation and coordination in cooperative multi-agent environment. Doctoral Thesis of Programming in Technology, Dissertation, University of GironaGoogle Scholar
  78. Talabeigi M, Forsati R, Meybodi MR (2010) A hybrid web recommender system based on cellular learning automata. In: IEEE international conference on granular computing, San Jose, California, pp 453–458Google Scholar
  79. Testlin ML (1961) On the behavior of finite automata in randim media. Autom Remote Control 22(10):1210–1219Google Scholar
  80. Theraulaz G, Bonabeau E, Deneubourg J (1998) Response threshold reinforcement and division of labour in insect societies. R Soc Lond Ser B Biol Sci 265:327–332Google Scholar
  81. Vafaeinezhad AR, Alesheikh AA, Hamrah M, Nourjou R, Shad R (2009) Using GIS to develop an efficient spatio-temporal task allocation algorithm to human groups in an entirely dynamic environment case study: earthquake rescue teams. In: Computational science and its applications—lecture notes in computer science, vol 5592, pp 66–78Google Scholar
  82. Yan Z, Jouandeau N, Cherif AA (2013) A survey and analysis of multi-robot coordination. Int J Adv Robot Syst 10:399CrossRefGoogle Scholar
  83. Yang Y, Zhou C, Tian Y (2009) Swarm robots task allocation based on response threshold model. In: IEEE international conference on autonomous robots and agents, pp 171–176Google Scholar
  84. Yasuda T, Kage K, Ohkura K (2014) Response threshold-based task allocation in a reinforcement learning robotic swarm. In: IEEE 7th International workshop on computational intelligence and applications, Hiroshima, Japan, pp 189–194Google Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Department of ComputingMacquarie UniversityNorth Ryde, NSWAustralia
  2. 2.Department of Computer EngineeringK.N. Toosi University of TechnologyTehranIran
  3. 3.Department of Computer Science and Research BranchIslamic Azad UniversityTehranIran

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