Abstract
The aim of this work is to produce and test a robust, distributed, multi-agent task allocation algorithm, as these are scarce and not well-documented in the literature. The vehicle used to create the robust system is the Performance Impact algorithm (PI), as it has previously shown good performance. Three different variants of PI are designed to improve its robustness, each using Monte Carlo sampling to approximate Gaussian distributions. Variant A uses the expected value of the task completion times, variant B uses the worst-case scenario metric and variant C is a hybrid that implements a combination of these. The paper shows that, in simulated trials, baseline PI does not handle uncertainty well; the task-allocation success rate tends to decrease linearly as degree of uncertainty increases. Variant B demonstrates a worse performance and variant A improves the failure rate only slightly. However, in comparison, the hybrid variant C exhibits a very low failure rate, even under high uncertainty. Furthermore, it demonstrates a significantly better mean objective function value than the baseline.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zhang, K., Collins Jr., E.G., Shi, D.: Centralized and distributed task allocation in multi-robot team via a stochastic clustering auction. ACM Trans. Autonom. Adapt. Syst. 7(2), 21 (2012)
Horling, B., Lesser, V.: A survey of multi-agent organizational paradigms. Knowl. Eng. Rev. 19, 281–316 (2004)
Zhao, W., Meng, Q., Chung, P.W.H.: A heuristic distributed task allocation method for multivehicle multitask problems and its application to search and rescue scenario. IEEE Trans. Cybern. 46(4), 902–915 (2015)
Choi, H.-L., Brunet, J., How, J.P.: Consensus-based decentralization auctions for robust task allocation. IEEE Trans. Robot. 25(4), 912–926 (2009)
Ponda, S.S.: Robust distributed planning strategies for autonomous multi-agent teams. PhD dissertation, Mass. Inst. Technol. (2012)
Grinstead, C.M., Snell, D.A.: Expected value and variance. In: Introduction to Probability, 2nd edn., pp. 936–953. American Mathematical Society, Rhode Island (1998)
Smith, R.G.: The contract net protocol: high level communication and control in a distributed problem solver. IEEE Trans. Comput. C-19(12), 1104–1113 (1998)
Pujol-Gonzalez, M., Cerquides, J., Meseguer, P., Rodríguez-Aguilar, J.A., Tambe, M.: Engineering the decentralized coordination of UAVs with limited communication range. In: Bielza, C., Salmerón, A., Alonso-Betanzos, A., Hidalgo, J.I., Martínez, L., Troncoso, A., Corchado, E., Corchado, J.M. (eds.) CAEPIA 2013. LNCS (LNAI), vol. 8109, pp. 199–208. Springer, Heidelberg (2013). doi:10.1007/978-3-642-40643-0_21
Lagoudakis, M., Markakis, E., Kempe, D., Keskinocak, P., KJleywegt, A., Koenig, S., Tovey, C., Meyerson, A., Jain, S.: Auction-Based Multi-Robot Routing. Robotics: Science and Systems, vol. 5, MIT Press (2005)
Fave, F.M.D., Farinelli, A., Rogers, A., Jennings, N.: A methodology for deploying the max-sum algorithm and a case study on unmanned aerial vehicles. In: Proceedings of IAAI-2012, pp. 2275–2280 (2012)
Khamis, A., Hussein, A., Elmogy A.: Multi-robot task allocation: a review of the state-of-the-art. In: Koubaa, A., Martinez-de Dios, J.R. (eds.) Cooperative Robots and Sensor Networks 2015: Studies in Computational Intelligence, vol. 604, pp. 31–51 (2015)
Bruno, J.L., Coffman, E.G., Sethi, R.: Scheduling independent tasks to reduce mean finishing time. Commun. ACM 17(7), 382–387 (1974)
Atay, N., Bayazit, B.: Mixed-integer linear programming solution to multi-robot task allocation problem. Technical Report 2006-54, Washington University, St. Louis (2006)
Gerkey, B.P., Matarić, M.J.M.: A formal analysis and taxonomy of task allocation in multi-robot systems. Intl. J. Robot. Res. 23(9), 939–954 (2004)
Glover, F., Marti, R.: Tabu search. In: Alba, E., Marti, R. (eds.) Metaheuristic Procedures for Training Neural Networks, pp. 53–69 (2006)
Shima, T., Rasmussen, S.J., Sparks, A.G., Passino, K.M.: Multiple task assignments for cooperating uninhabited aerial vehicles using genetic algorithms. Comput. Oper. Res. 33(11), 3252–3269 (2006)
Oliver, G., Guerrero, J.: Auction and swarm multi-robot task allocation algorithms in real time scenarios. In: Yasuda, T. (ed.) Multi-Robot Systems, Trends and Development, pp. 437–456 (2011)
Dias, M.B., Stentz, A.: Opportunistic optimization for market-based multi-robot control. In: Proceedings of IROS-2002, pp. 2714–2720 (2002)
Bertsekas, D.P.: The auction algorithm for assignment and other network flow problems. Technical Report, Mass. Inst. Technol., Cambridge, MA (1989)
Dias, M., Zlot, R., Kalra, N., Stentz, A.: Market-based multirobot coordination: a survey and analysis. Proc. IEEE 94(7), 1257–1270 (2006)
Coltin, B., Veloso, M.: Mobile robot task allocation in hybrid wireless sensor networks. In: Proceedings of IROS-2010, pp. 2932–2937 (2010)
Cramton, P., Shoham, Y., Steinberg, R.: An overview of combinatorial auction. ACM SIGecom Exchanges 7(1), 3–14 (2007)
Whitbrook, A., Meng, Q., Chung, P.W.H.: A novel distributed scheduling algorithm for time-critical, multi-agent systems. In: Proceedings of IROS-2015, pp. 6451–6458 (2015)
Undurti, A., How, J.P.: A decentralized approach to multi-agent planning in the presence of constraints and uncertainty. In: Proceedings of ICRA-2011, pp. 2534–2539 (2011)
Musliner, D.J., Durfee, E.H., Wu, J., Dolgov, D.A., Goldman, R.P., Boddy, M.S.: Coordinated plan management using multiagent MDPs. In: Proceedings of SSDPSM (2006)
Maheswaran, R.T., Rogers, C.M., Sanchez, R., Szekely, P.: Realtime multi-agent planning and scheduling in dynamic uncertain domains. In: Proceedings of ICAPS (2010)
Ramchurn, S.D., Fischer, J.E., Ikuno, Y., Wu, F., Flann, J., Waldock, A.: A study of human-agent collaboration for Multi-UAV task allocation in dynamic environments. In: Proceedings of IJCAI (2015)
Liu, L., Shell, D.A.: Assessing optimal assignment under uncertainty: an interval-based algorithm. Int. J. Rob. Res. 30(7), 936–953 (2011)
Turner, J., Meng, Q., Schaeffer, G.: Increasing allocated tasks with a time minimization algorithm for a search and rescue scenario. In: Proceedings of ICRA 2015, pp. 3401–3407 (2015)
Whitbrook, A., Meng, Q., Chung, P.W.H.: Reliable, distributed scheduling and rescheduling for time-critical, multi-agent systems. IEEE Trans. Autom. Sci. Eng. (2017, to appear)
Collinson, R.G.P.: Introduction to Avionic Systems, 3rd edn. Springer, London (2011)
Huston, W.B.: Accuracy of airspeed measurements and flight calibration procedures. Technical Report No. 919, NACA, Langley Memorial Aeronautical Laboratory (1948)
Acknowledgments
This work was supported by EPSRC (grant number EP/J011525/1) with BAE Systems as the leading industrial partner.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Whitbrook, A., Meng, Q., Chung, P.W.H. (2017). A Robust, Distributed Task Allocation Algorithm for Time-Critical, Multi Agent Systems Operating in Uncertain Environments. In: Benferhat, S., Tabia, K., Ali, M. (eds) Advances in Artificial Intelligence: From Theory to Practice. IEA/AIE 2017. Lecture Notes in Computer Science(), vol 10351. Springer, Cham. https://doi.org/10.1007/978-3-319-60045-1_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-60045-1_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-60044-4
Online ISBN: 978-3-319-60045-1
eBook Packages: Computer ScienceComputer Science (R0)