We show how detailed simulation models and abstract Markov models can be developed collaboratively to generate and implement effective controllers for autonomous agent search and retrieve missions. We introduce a concrete simulation model of an Unmanned Aerial Vehicle (UAV). We then show how the probabilistic model checker PRISM is used for optimal strategy synthesis for a sequence of scenarios relevant to UAVs and potentially other autonomous agent systems. For each scenario we demonstrate how it can be modelled using PRISM, give model checking statistics and present the synthesised optimal strategies. We then show how our strategies can be returned to the controller for the simulation model and provide experimental results to demonstrate the effectiveness of one such strategy. Finally we explain how our models can be adapted, using symmetry, for use on larger search areas, and demonstrate the feasibility of this approach.

中文翻译：

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自主系统控制器综合的协作模型

我们展示了如何协同开发详细的仿真模型和抽象的马尔可夫模型，以生成和实施有效的控制器，用于自主代理搜索和检索任务。我们介绍了一个无人机（UAV）的具体仿真模型。然后，我们展示了概率模型检查器 PRISM 如何用于与无人机和潜在的其他自主代理系统相关的一系列场景的最佳策略合成。对于每个场景，我们都展示了如何使用 PRISM 对其进行建模，给出模型检查统计数据并展示综合的最优策略。然后，我们展示了如何将我们的策略返回给模拟模型的控制器，并提供实验结果来证明这种策略的有效性。最后，我们解释了如何使用对称性来调整我们的模型，