KDI: Automated Learning in Network Traffic Control

9873469Mishra, Bhubaneswar, Parikh, Rohit纽约大学KDI：网络流量控制中的自动化学习这是一项适用于控制和优化大规模网络中资源分配的自动化智能代理的设计和实现研究。 该区域的特点是连接稀疏的计算元素，仅通过在大规模分布式环境中使用联合资源进行交互。 该研究项目的数学基础是经济学。 然而，与经典理论相反，这里策略选择的基础计算模型涉及归纳学习和发现过程。 这里的目标是通过使用信念修正逻辑来建模基于信念的学习，对自动化代理的推理行为进行严格的处理。 所提出的研究的长期意义是传播适合大规模、地理分布式网络中的控制和优化的自动化代理，而无需了解系统状态的常识。

9873469Mishra, Bhubaneswar, Parikh, RohitNew York UniversityKDI: Automated Learning in Network Traffic ControlThis is a design and implementation study of automated intelligent agents suitable for controlling and optimizing resource allocation in large-scale networks. This area is characterized by sparsely-connected, computational elements that interact solely through the use of joint resources in massively-distributed environments. The mathematical underpinnings of this research program are found in economics. In contrast to classical theory, however, the underlying computational model of strategy selection here involves inductive learning and discovery processes. The here goal is to provide a rigorous treatment of the reasoning behavior of automated agents by using belief-revision logics to model belief-based learning. The long term significance of the proposed research is the dissemination of automated agents suitable for control and optimization in large-scale, geographically distributed networks, without the benefit of common knowledge of the state of the system.