Resource allocation and task prioritisation are key problem domains in the fields of autonomous vehicles, networking, and cloud computing. The challenge in developing efficient and robust algorithms comes from the dynamic nature of these systems, with many components communicating and interacting in complex ways. The multi-group resource allocation optimisation (MG-RAO) algorithm we present uses multiple function approximations of resource demand over time, alongside reinforcement learning techniques, to develop a novel method of optimising resource allocation in these multi-agent systems. This method is applicable where there are competing demands for shared resources, or in task prioritisation problems. Evaluation is carried out in a simulated environment containing multiple competing agents. We compare the new algorithm to an approach where child agents distribute their resources uniformly across all the tasks they can be allocated. We also contrast the performance of the algorithm where resource allocation is modelled separately for groups of agents, as to being modelled jointly over all agents. The MG-RAO algorithm shows a 23 - 28% improvement over fixed resource allocation in the simulated environments. Results also show that, in a volatile system, using the MG-RAO algorithm configured so that child agents model resource allocation for all agents as a whole has 46.5% of the performance of when it is set to model multiple groups of agents. These results demonstrate the ability of the algorithm to solve resource allocation problems in multi-agent systems and to perform well in dynamic environments.
EcoNet: Multiagent Planning and Control Of Household Energy Resources Using Active Inference
VERSES, Los Angeles, CA, USA（VERSES, 美国洛杉矶州）；Spatial Web Foundation, Los Angeles, CA, USA（空间网络基金会, 美国洛杉矶州）；Dept. of Engineering and Informatics, Univ. of Sussex, Brighton, UK（工程与信息学系, 英国苏塞克斯大学）；Institut Sante et societe, UQAM, Montreal, CAN（健康与社会研究所, 加拿大蒙特利尔）；Macquarie University, Sydney, AUS（麦考瑞大学, 澳大利亚悉尼）；LearnableLoopAI.com, Custer, WA, USA（LearnableLoopAI.com, 美国华盛顿州）；Queen Square Institute of Neurology, UCL, London, UK（queen square 神经学研究所, 英国伦敦大学学院）
A Multiagent Path Search Algorithm for Large-Scale Coalition Structure Generation
Clermont Auvergne University（克莱蒙奥弗涅大学）；Clermont Auvergne INP（克莱蒙奥弗涅国立综合理工学院）；CNRS（法国国家科学研究中心）；LIMOS（利莫什科学与技术实验室）；Univ Lyon（里昂大学）；UCBL（里昂第一大学）；INSA Lyon（里昂国立应用科学学院）；Centrale Lyon（里昂中央理工学院）；Univ Lyon 2（里昂第二大学）；LIRIS（里昂信息、图像与智能系统实验室）；Laboratory of Applied Mathematics, Faculty of Exact Sciences, University of Bejaia（贝贾亚大学精确科学学院应用数学实验室）；TCG Centres for Research and Education in Science and Technology（TCG科学技术研究与教育中心）；Carnegie Mellon University（卡内基梅隆大学）；Strategy Robot, Inc.（Strategy Robot公司）；Strategic Machine, Inc.（Strategic Machine公司）；Optimized Markets, Inc.（Optimized Markets公司）
