Various applications for inter-machine communications are on the rise. Whether it is for autonomous driving vehicles or the internet of everything, machines are more connected than ever to improve their performance in fulfilling a given task. While in traditional communications the goal has often been to reconstruct the underlying message, under the emerging task-oriented paradigm, the goal of communication is to enable the receiving end to make more informed decisions or more precise estimates/computations. Motivated by these recent developments, in this paper, we perform an indirect design of the communications in a multi-agent system (MAS) in which agents cooperate to maximize the averaged sum of discounted one-stage rewards of a collaborative task. Due to the bit-budgeted communications between the agents, each agent should efficiently represent its local observation and communicate an abstracted version of the observations to improve the collaborative task performance. We first show that this problem can be approximated as a form of data-quantization problem which we call task-oriented data compression (TODC). We then introduce the state-aggregation for information compression algorithm (SAIC) to solve the formulated TODC problem. It is shown that SAIC is able to achieve near-optimal performance in terms of the achieved sum of discounted rewards. The proposed algorithm is applied to a geometric consensus problem and its performance is compared with several benchmarks. Numerical experiments confirm the promise of this indirect design approach for task-oriented multi-agent communications.
Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications
Imperial College London（帝国理工学院）；Tsinghua University（清华大学）；Amazon（亚马逊）；Virginia Tech（弗吉尼亚理工学院）；Zhejiang Lab（之江实验室）；Zhejiang University（浙江大学）；The Ohio State University（俄亥俄州立大学）；University College London（伦敦大学学院）；Yonsei University（延世大学）
Distributed Electromagnetic Neural Networks for Task-Oriented Semantic Communications
University of Electronic Science and Technology of China（电子科技大学）；The University of British Columbia（不列颠哥伦比亚大学）；University of Oulu（奥卢大学）；Khalifa University（哈利法大学）
Aligning Task- and Reconstruction-Oriented Communications for Edge Intelligence
School of Computing Science, University of Glasgow（格拉斯哥大学计算科学学院）；University of Manchester（曼彻斯特大学）；James Watt School of Engineering, University of Glasgow（格拉斯哥大学詹姆斯·瓦特工程学院）；School of Electrical and Information Engineering, University of Sydney（悉尼大学电气与信息工程学院）
Task-Oriented Sensing, Computation, and Communication Integration for Multi-Device Edge AI
ShanghaiTech University（上海科技大学）；Shenzhen Research Institute of Big Data（深圳大数据研究院）；Peking University（北京大学）；The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳））；Weizmann Institute of Science（魏茨曼科学研究所）；Peng Cheng Laboratory（鹏城实验室）
