Article

Dynamic multi-agent task allocation with spatial and temporal constraints

Authors:

Steven Okamoto,

Roie ZivanAuthors Info & Claims

AAAI'14: Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence

Pages 1384 - 1390

Published: 27 July 2014 Publication History

Abstract

Realistic multi-agent team applications often feature dynamic environments with soft deadlines that penalize late execution of tasks. This puts a premium on quickly allocating tasks to agents, but finding the optimal allocation is NP-hard due to temporal and spatial constraints that require tasks to be executed sequentially by agents.

We propose FMC TA, a novel task allocation algorithm that allows tasks to be easily sequenced to yield high-quality solutions. FMC_TA first finds allocations that are fair (envyfree), balancing the load and sharing important tasks between agents, and efficient (Pareto optimal) in a simplified version of the problem. It computes such allocations in polynomial or pseudo-polynomial time (centrally or distributedly, respectively) using a Fisher market with agents as buyers and tasks as goods. It then heuristically schedules the allocations, taking into account inter-agent constraints on shared tasks.

We empirically compare our algorithm to state-of-the-art incomplete methods, both centralized and distributed, on law enforcement problems inspired by real police logs. The results show a clear advantage for FMC TA both in total utility and in other measures commonly used by law enforcement authorities.

References

[1]

Arshad, M., and Silaghi, M. C. 2004. Distributed simulated annealing. Distributed Constraint Problem Solving and Reasoning in Multi-Agent Systems, Frontiers in Artificial Intelligence and Applications series112.

[2]

Devanur, N. R.; Papadimitriou, C. H.; Saberi, A.; and Vazirani, V. V. 2002. Market equilibrium via a primal-dual-type algorithm. In FOCS '02: Proceedings of the 43rd Symposium on Foundations of Computer Science, 389-395.

[3]

Farinelli, A.; Rogers, A.; Petcu, A.; and Jennings, N. R. 2008. Decentralised coordination of low-power embedded devices using the max-sum algorithm. In Proceedings of the 7th International Conference on Autonomous Agents and Multiagent Systems (AAMAS-08), 639-646.

[4]

Gale, D. 1960. The Theory of Linear Economic Models. McGraw-Hill.

[5]

Jones, E. G.; Dias, M. B.; and Stentz, A. 2007. Learning-enhanced market-based task allocation for oversubscribed domains. In Proceedings of the 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[6]

Macarthur, K.; Stranders, R.; Ramchurn, S.; and Jennings, N. 2011. A distributed anytime algorithm for dynamic task allocation in multi-agent systems. In Proceedings of the 25th Conference on Artificial Intelligence (AAAI-11), 701-706. AAAI Press.

[7]

Maheswaran, R. T.; Tambe, M.; Bowring, E.; Pearce, J. P.; and Varakantham, P. 2004. Distributed algorithms for DCOP: A graphical-game-based approach. In Proceedings of the 17th International Conference on Parallel and Distributed Computing Systems, 15-17.

[8]

Poole, D., and Mackworth, A. K. 2010. Artificial Intelligence - Foundations of Computational Agents. Cambridge University Press.

[9]

Ramchurn, S. D.; Polukarov, M.; Farinelli, A.; Truong, C.; and Jennings, N. R. 2010a. Coalition formation with spatial and temporal constraints. In Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems (AAMAS-10), 1181-1188.

[10]

Ramchurn, S. D.; Farinelli, A.; Macarthur, K. S.; and Jennings, N. R. 2010b. Decentralized coordination in RoboCup Rescue. The Computer Journal 53(9):1447-1461.

[11]

Reeves, C. R., ed. 1993. Modern heuristic techniques for combinatorial problems. New York, NY, USA: John Wiley & Sons, Inc.

[12]

Reijnierse, J. H., and Potters, J. A. M. 1998. On finding an envy-free Pareto-optimal division. Mathematical Programming 83:291-311.

[13]

Schoneveld, A.; de Ronde, J. F.; and Sloot, P. M. A. 1997. On the complexity of task allocation. Journal of Complexity 3:52-60.

[14]

Walsh, W., and Wellman, M. 1998. A market protocol for decentralized task allocation. In Proceedings of the International Conference on Multi-Agent Systems, 325-332.

[15]

Zhang, W.; Xing, Z.; Wang, G.; and Wittenburg, L. 2005. Distributed stochastic search and distributed breakout: properties, comparison and applications to constraints optimization problems in sensor networks. Artificial Intelligence 161:1-2:55-88.

[16]

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

Cited By

Lavie MCaspi TLev OZivan RAgmon NAn BRicci AYeoh W(2023)Ask and You Shall be Served: Representing & Solving Multi-agentOptimization Problems with Service Requesters and ProvidersProceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems10.5555/3545946.3598622(77-85)Online publication date: 30-May-2023
https://dl.acm.org/doi/10.5555/3545946.3598622
Amador Nelke SZivan RLarson KWinikoff MDas SDurfee E(2017)Incentivizing Cooperation between Heterogeneous Agents in Dynamic Task AllocationProceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems10.5555/3091125.3091278(1082-1090)Online publication date: 8-May-2017
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AAAI'14: Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence

July 2014

3155 pages

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AAAI Press

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Published: 27 July 2014

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Cited By

Lavie MCaspi TLev OZivan RAgmon NAn BRicci AYeoh W(2023)Ask and You Shall be Served: Representing & Solving Multi-agentOptimization Problems with Service Requesters and ProvidersProceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems10.5555/3545946.3598622(77-85)Online publication date: 30-May-2023
https://dl.acm.org/doi/10.5555/3545946.3598622
Amador Nelke SZivan RLarson KWinikoff MDas SDurfee E(2017)Incentivizing Cooperation between Heterogeneous Agents in Dynamic Task AllocationProceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems10.5555/3091125.3091278(1082-1090)Online publication date: 8-May-2017
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Claes DOliehoek FBaier HTuyls KLarson KWinikoff MDas SDurfee E(2017)Decentralised Online Planning for Multi-Robot Warehouse CommissioningProceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems10.5555/3091125.3091198(492-500)Online publication date: 8-May-2017
https://dl.acm.org/doi/10.5555/3091125.3091198
Nunes EManner MMitiche HGini M(2017)A taxonomy for task allocation problems with temporal and ordering constraintsRobotics and Autonomous Systems10.1016/j.robot.2016.10.00890:C(55-70)Online publication date: 1-Apr-2017
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Parker JGini M(2016)Controlling growing tasks with heterogeneous agentsProceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence10.5555/3060621.3060686(461-467)Online publication date: 9-Jul-2016
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Claes DRobbel POliehoek FTuyls KHennes Dvan der Hoek WWeiss GYolum PBordini RElkind E(2015)Effective Approximations for Multi-Robot Coordination in Spatially Distributed TasksProceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems10.5555/2772879.2773265(881-890)Online publication date: 4-May-2015
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