Dynamic Multi-Agent Task Allocation with Spatial and Temporal Constraints

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.

Authors

• Sofia Amador Ben-Gurion University of the Negev
• Steven Okamoto Ben-Gurion University of the Negev
• Roie Zivan Ben-Gurion University of the Negev

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