Towards generating and executing automatically simulation experiments

仿真研究越来越成为许多应用领域不可或缺的工具。然而，进行仿真研究不仅需要对要建模和分析的系统有深入的了解，还需要对仿真实验的设计和所涉及的方法有详细的了解。“自动生成和执行模拟实验”项目旨在通过自动生成和执行实验来支持系统的离散事件随机模拟研究。模拟研究涉及模型的迭代改进和各种实验的连续执行，例如，敏感性分析或优化，其中也有不同的方法可用。为了自动生成和执行仿真实验，我们将重点关注以下问题：需要哪些关于仿真实验、方法、目标和当前仿真研究的知识，这些知识如何表示、使用和组合，以及仿真实验的图式、方法本体、概念模型以及以前执行的仿真实验和出处在这一努力中起什么作用？我们将采用两种策略来生成仿真实验。两者都依赖于上述知识来源的有效组合。然而，他们的出发点和方法各不相同。一种策略侧重于生成和执行特定的模拟实验，其基础是之前已经执行过类似目标的实验，或者基于使用密切相关模型完成的某些实验。因此，来源将成为这一策略的起点。第二种策略旨在从头开始生成仿真实验，并依靠推理规则选择实验类型，适当的方法，并填充相应的实验模板。在这里，概念模型将是至关重要的，因为它提供了关于仿真模型的上下文信息。一些具体的模拟研究将有助于评估已开发的方法、策略及其组合。

Simulation studies are becoming more and more an indispensable tool in many application areas. However, executing simulation studies requires not only in-depth knowledge about the system to be modeled and analyzed, but also detailed knowledge about the design of simulation experiments and the involved methods. The project "towards GeneRating and Executing Automatically Simulation Experiments - GrEASE" aims at supporting systematic discrete-event stochastic simulation studies by automatically generating and executing experiments. Simulation studies involve the iterative refinement of models and the successive execution of diverse experiments, e.g., sensitivity analysis or optimization, for which again different methods are available. To automatically generate and execute simulation experiments, we will focus on the following questions: what kinds of knowledge about simulation experiments, methods, goals, and about the current simulation study are needed, how can such knowledge be represented, used, and combined, and what role can schemas of simulation experiments, ontologies about methods, the conceptual model, as well as previously executed simulation experiments and provenance play in this endeavor? We will pursue two strategies to generate simulation experiments. Both rely on an effective combination of the above knowledge sources. However, their starting points and approaches vary. One strategy focuses on generating and executing a specific simulation experiment on the basis that experiments with similar goals have been executed before, or on the basis that certain experiments have been done with closely related models. Thus, provenance will form the starting point for this strategy. The second strategy aims to generate simulation experiments from scratch and will rely on inference rules to select an experiment type, appropriate methods, and to fill the respective experiment templates. Here, the conceptual model will be crucial as it provides context information about the simulation model. Several specific simulation studies will help evaluating the developed methods, strategies, and their combination.