具有时空约束的随时高效联盟形成
Anytime and Efficient Coalition Formation with Spatial and Temporal Constraints
摘要：
具有时空约束的联盟形成问题（CFSTP）是一个多智能体任务调度问题，其中任务在空间上分布，具有截止期限和工作量，智能体的数量通常远小于任务的数量，因此智能体必须形成联盟以最大化完成任务的数量。当前最先进的CFSTP求解器，具有前瞻性的联盟形成（CFLA）算法，具有两个主要限制。首先，它的时间复杂度是指数的代理的数量。第二，正如我们所展示的，它的前瞻技术在现实世界的场景中是无效的，例如开放的多智能体系统，新的任务可以随时出现。在这项工作中，我们研究了它的设计，并定义了一个扩展，称为联盟形成与改进的前瞻性（CFLA 2），它实现了更好的性能。由于我们不能消除CFLA在CFLA 2中的局限性，我们还开发了一种新的算法来解决CFSTP，第一个是随时，有效的，并具有可证明的保证，称为基于竞争者的联盟形成（CCF）。我们的经验表明，在设置的前瞻性技术是非常有效的，CCF完成高达30%（分别。10%）的任务比CFLA（分别为。CFLA2），同时快达四个数量级。我们的研究结果肯定CCF作为新的国家的最先进的算法来解决CFSTP。
英文摘要：
The Coalition Formation with Spatial and Temporal constraints Problem (CFSTP) is a multi-agent task scheduling problem where the tasks are spatially distributed, with deadlines and workloads, and the number of agents is typically much smaller than the number of tasks, thus the agents have to form coalitions in order to maximise the number of completed tasks. The current state-of-the-art CFSTP solver, the Coalition Formation with Look-Ahead (CFLA) algorithm, has two main limitations. First, its time complexity is exponential with the number of agents. Second, as we show, its look-ahead technique is not effective in real-world scenarios, such as open multi-agent systems, where new tasks can appear at any time. In this work, we study its design and define an extension, called Coalition Formation with Improved Look-Ahead (CFLA2), which achieves better performance. Since we cannot eliminate the limitations of CFLA in CFLA2, we also develop a novel algorithm to solve the CFSTP, the first to be anytime, efficient and with provable guarantees, called Cluster-based Coalition Formation (CCF). We empirically show that, in settings where the look-ahead technique is highly effective, CCF completes up to 30% (resp. 10%) more tasks than CFLA (resp. CFLA2) while being up to four orders of magnitude faster. Our results affirm CCF as the new state-of-the-art algorithm to solve the CFSTP.
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