This paper outlines two approaches|based on counterexample-guided abstraction refinement (CEGAR) and counterexample-guided inductive synthesis (CEGIS), respectively to the automated synthesis of finite-state probabilistic models and programs. Our CEGAR approach iteratively partitions the design space starting from an abstraction of this space and refines this by a light-weight analysis of verification results. The CEGIS technique exploits critical subsystems as counterexamples to prune all programs behaving incorrectly on that input. We show the applicability of these synthesis techniques to sketching of probabilistic programs, controller synthesis of POMDPs, and software product lines.

中文翻译：

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模型修复改造：关于马尔可夫链的自动合成

本文概述了两种基于反例引导抽象精化（CEGAR）和反例引导归纳合成（CEGIS）的方法，分别用于有限状态概率模型和程序的自动合成。我们的 CEGAR 方法从该空间的抽象开始迭代地划分设计空间，并通过对验证结果的轻量级分析对其进行改进。CEGIS 技术利用关键子系统作为反例来修剪所有在该输入上行为不正确的程序。我们展示了这些综合技术对概率程序草图、POMDP 控制器综合和软件产品线的适用性。