An emerging branch of control theory specialises in certificate learning,
concerning the specification of a desired (possibly complex) system behaviour
for an autonomous or control model, which is then analytically verified by
means of a function-based proof. However, the synthesis of controllers abiding
by these complex requirements is in general a non-trivial task and may elude
the most expert control engineers. This results in a need for automatic
techniques that are able to design controllers and to analyse a wide range of
elaborate specifications. In this paper, we provide a general framework to
encode system specifications and define corresponding certificates, and we
present an automated approach to formally synthesise controllers and
certificates. Our approach contributes to the broad field of safe learning for
control, exploiting the flexibility of neural networks to provide candidate
control and certificate functions, whilst using SMT-solvers to offer a formal
guarantee of correctness. We test our framework by developing a prototype
software tool, and assess its efficacy at verification via control and
certificate synthesis over a large and varied suite of benchmarks.

我们提供了一个对系统规范进行编码和定义相关证明的通用框架，并提出了一种自动化的方法来形式合成控制器和证明，通过利用神经网络的灵活性提供候选控制和证明函数，同时使用 SMT 求解器提供正确性的正式保证，我们通过开发一个原型软件工具对我们的框架进行测试，并评估其在通过对广泛的基准测试进行控制和证明合成时的有效性。

动态和控制模型的通用验证框架与证书合成

A General Verification Framework for Dynamical and Control Models via  Certificate Synthesis

Adaptive experimental design methods are increasingly being used in industry
as a tool to boost testing throughput or reduce experimentation cost relative
to traditional A/B/N testing methods. This paper shares lessons learned
regarding the challenges and pitfalls of naively using adaptive experimentation
systems in industrial settings where non-stationarity is prevalent, while also
providing perspectives on the proper objectives and system specifications in
these settings. We developed an adaptive experimental design framework for
counterfactual inference based on these experiences, and tested it in a
commercial environment.

本文讨论了使用自适应实验设计方法在非稳态的工业环境下进行实验的挑战和陷阱，提供了在这些环境中的正确目标和系统规格的不同观点，并基于这些经验开发了一个自适应实验设计框架用于对照推断，并在商业环境中进行了测试。