Allocation and planning with a collection of tasks and a group of agents is an important problem in multiagent systems. One commonly faced bottleneck is scalability, as in general the multiagent model increases exponentially in size with the number of agents. We consider the combination of random task assignment and multiagent planning under multiple-objective constraints, and show that this problem can be decentralised to individual agent-task models. We present an algorithm of point-oriented Pareto computation, which checks whether a point corresponding to given cost and probability thresholds for our formal problem is feasible or not. If the given point is infeasible, our algorithm finds a Pareto-optimal point which is closest to the given point. We provide the first multi-objective model checking framework that simultaneously uses GPU and multi-core acceleration. Our framework manages CPU and GPU devices as a load balancing problem for parallel computation. Our experiments demonstrate that parallelisation achieves significant run time speed-up over sequential computation.

翻译：任务分配与多智能体规划是多智能体系统中的一个重要问题，涉及一组任务与一组智能体的协同分配与规划。常见瓶颈在于可扩展性：一般而言，多智能体模型的规模随智能体数量呈指数增长。本文考虑随机任务分配与多目标约束下多智能体规划的结合，并证明该问题可解耦为单个智能体-任务模型。我们提出一种面向点的帕累托计算方法，用于检验形式化问题中给定成本与概率阈值对应的点是否可行。若给定点不可行，算法将寻找距离该点最近的帕累托最优解。我们首次提出同时利用GPU与多核加速的多目标模型检测框架，该框架将CPU与GPU设备管理转化为并行计算的负载均衡问题。实验表明，并行化相较于顺序计算实现了显著的运行时间加速。