多任务ML系统异步协同扩展的多智能体框架
A Multiagent Framework for the Asynchronous and Collaborative Extension of Multitask ML Systems
摘要：
传统的机器学习开发方法无法让大量的贡献者（每个人都有不同的目标）共同创建和扩展共享的智能系统。启用这种协作方法可以加快创新速度，增加ML技术的可访问性，并实现新功能的出现。我们相信，这种新颖的ML开发方法可以通过ML模型的模块化表示和新颖抽象的定义来证明，这些抽象允许实现和执行用于异步使用和扩展模块化智能系统的各种方法。我们提出了一个多智能体框架的协作和异步扩展的动态大规模多任务系统。
英文摘要：
The traditional ML development methodology does not enable a large number of contributors, each with distinct objectives, to work collectively on the creation and extension of a shared intelligent system. Enabling such a collaborative methodology can accelerate the rate of innovation, increase ML technologies accessibility and enable the emergence of novel capabilities. We believe that this novel methodology for ML development can be demonstrated through a modularized representation of ML models and the definition of novel abstractions allowing to implement and execute diverse methods for the asynchronous use and extension of modular intelligent systems. We present a multiagent framework for the collaborative and asynchronous extension of dynamic large-scale multitask systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
