Task Allocation and Reallocation for Heterogeneous Multiagent Systems Based on Potential Game
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摘要: 针对异构多智能体系统, 基于势博弈理论提出一种新的任务分配和重分配算法. 考虑任务执行同步性和任务时效性的多重约束, 导致异构多智能体系统中各个体任务执行时间受到多种限制, 建立一个基于势博弈的算法结构, 使系统以分布式方式工作. 在此基础上, 基于势博弈理论设计任务分配算法, 保证在较低复杂度的同时, 可以得到近似最大化期望全局效用的良好分配方案, 并且随后将所提出的方法推广到任务重分配方案实现故障下的容错. 最后, 针对攻击任务场景对所提算法进行仿真验证, 结果表明, 在期望全局效用、容错能力和算法复杂度方面具有全面的性能.Abstract: This paper presents a novel task allocation and reallocation algorithm for heterogeneous multiagent systems based on potential game theory. Considering the multiple constraints of task execution synchronization and task timeliness, which lead to various restrictions on the execution time of each individual task in the heterogeneous multiagent systems, a potential game-based algorithm structure is established to make agents work in a distributed manner. On this basis, the task allocation algorithm is designed based on potential game theory, which produces a promising solution that nearly maximizes the expected global utility and guarantees lower complexity, and afterwards the fault tolerance is achieved by generalizing the proposed algorithm to task reallocation schemes. Finally, the proposed algorithm is verified by simulation of the attack task scenario, and results reveal the comprehensive performance in terms of the expected global utility, the fault tolerance and the algorithm complexity.
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Key words:
- Task allocation /
- multiagent systems /
- potential game /
- constrained optimization /
- fault tolerance
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图 1 势博弈次优解的选择过程
Fig. 1 The process of selecting the suboptimal solution of the potential game
图 2 健康环境下的初始任务分配流程图
Fig. 2 The flow chart of the initial task allocation in the healthy environment
图 5 分配单项任务的最大期望效用对比
Fig. 5 Comparison of the maximum expected utilities for allocating a single task
图 6 分配单项任务的所需迭代次数对比
Fig. 6 Comparison of the required number of iterations for allocating a single task
图 7 不同任务数量下的最大期望全局效用衰减
Fig. 7 Reductions of the maximum expected global utility under different number of tasks
图 8 不同智能体数量下的最大期望全局效用衰减
Fig. 8 Reductions of the maximum expected global utility under different number of agents
图 9 所提出算法与枚举法迭代次数的比值
Fig. 9 The ratio of the number of iterations between the proposed algorithm and the enumeration method
表 1 智能体初始信息
Table 1 Initial information of agents
智能体$A_i$ 位置 (m, m) 能力$(\lambda_{i1},\lambda_{i2},\lambda_{i3},\lambda_{i4})$ 最大速度 (m/s) 最大加速度 (m/s2) $A_1$ (0, 0) (0.5, 0.5, 1.0, 1.0) 10 0.25 $A_2$ (1000, 0) (1.0, 1.0, 0.5, 0.5) 20 0.25 $A_3$ (3000, 0) (0.5, 1.0, 1.0, 0.5) 10 0.25 $A_4$ (6000, 6000) (1.0, 0.5, 0.5, 1.0) 20 0.25 
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表 2 任务初始信息
Table 2 Initial information of tasks
任务$T_j$ 位置 (m, m) 基础效用$r_j$ 所需智能体数量$N_j$ 折扣系数$\mu_j$ $T_1$ (4000, 4000) 10 2 5$\times 10^{-4}$ $T_2$ (2000, 2000) 20 2 5$\times 10^{-4}$ $T_3$ (0, 2000) 10 2 5$\times 10^{-4}$ $T_4$ (2000, 4000) 10 2 5$\times 10^{-4}$
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