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Sofia Amador

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JAAMAS Journal 2021 Journal Article

Towards addressing dynamic multi-agent task allocation in law enforcement

  • Itshak Tkach
  • Sofia Amador

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 ( LEP H ), 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 LEP H problem by using and comparing three methods: Fisher market-based FMC_TA H +, swarm intelligence HDBA, and Simulated Annealing SA algorithms. FMC_TA H + 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_TA H + 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_TA H + both in total utility and in the average arrival time to tasks. Also, compared respectively to HDBA and SA, FMC_TA H + leads to 34% and 32% higher team utility in the highest shift workload.

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AAAI Conference 2014 Conference Paper

Dynamic Multi-Agent Task Allocation with Spatial and Temporal Constraints

  • Sofia Amador
  • Steven Okamoto
  • Roie Zivan

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.

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