论文标题
塞拉：研究自动化和可重复性的模块化框架
SIERRA: A Modular Framework for Research Automation and Reproducibility
论文作者
论文摘要
现代智能系统研究人员形成了有关系统行为的假设，然后使用一个或多个独立变量来检验其假设进行实验。我们提出了Sierra，这是一个围绕加速研究开发和改善结果可重复性的想法的新颖框架。 Sierra通过自动化从自变量，执行实验的查询中生成可执行实验的过程来加速研究，并处理结果以生成可交付成果，例如图形和视频。它将测试假设的范式从程序（“执行这些步骤回答查询”）转移到声明性（“这是要测试的查询 - go！”），从而减少了研究人员的负担。它采用模块化体系结构，可轻松自定义和扩展各个研究人员的需求，从而消除了手动配置和通过抛弃脚本进行处理。 Sierra通过提供独立于执行环境（HPC硬件，真实机器人等）和目标平台（任意模拟器或真实的机器人）的执行环境（HPC硬件，真实机器人等）来提高研究的可重复性。这可以使精确的实验复制，直至执行环境和平台的极限，并使研究人员易于测试不同计算环境中的假设。
Modern intelligent systems researchers form hypotheses about system behavior and then run experiments using one or more independent variables to test their hypotheses. We present SIERRA, a novel framework structured around that idea for accelerating research development and improving reproducibility of results. SIERRA accelerates research by automating the process of generating executable experiments from queries over independent variables(s), executing experiments, and processing the results to generate deliverables such as graphs and videos. It shifts the paradigm for testing hypotheses from procedural ("Do these steps to answer the query") to declarative ("Here is the query to test--GO!"), reducing the burden on researchers. It employs a modular architecture enabling easy customization and extension for the needs of individual researchers, thereby eliminating manual configuration and processing via throw-away scripts. SIERRA improves reproducibility of research by providing automation independent of the execution environment (HPC hardware, real robots, etc.) and targeted platform (arbitrary simulator or real robots). This enables exact experiment replication, up to the limit of the execution environment and platform, as well as making it easy for researchers to test hypotheses in different computational environments.
