多目标任务分配与多智能体规划：混合GPU-CPU加速
Multi-Objective Task Assignment and Multiagent Planning with Hybrid GPU-CPU Acceleration
AI总结：
针对多智能体系统中任务分配与规划的规模瓶颈，提出面向点的帕累托计算算法，并构建首个同时利用GPU和多核加速的多目标模型检测框架，实现显著加速。
摘要：
在由一组任务和一组智能体组成的系统中，分配与规划是多智能体系统中的一个重要问题。一个常见的瓶颈是可扩展性，因为通常多智能体模型的规模会随智能体数量呈指数增长。我们考虑了在多重目标约束下随机任务分配与多智能体规划的组合问题，并证明该问题可以分散为单个智能体-任务模型。我们提出了一种面向点的帕累托计算算法，该算法检查对于给定成本和概率阈值，我们的形式化问题中对应的点是否可行。如果给定点不可行，我们的算法会找到一个距离该给定点最近的帕累托最优点。我们提供了第一个同时使用GPU和多核加速的多目标模型检测框架。我们的框架将CPU和GPU设备的管理视为一个用于并行计算的负载均衡问题。我们的实验表明，并行化相对于顺序计算实现了显著的运行时间加速。
英文摘要：
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.
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