论文标题
安全和适应性的决策，以优化安全 - 关键系统：ARTEO算法
Safe and Adaptive Decision-Making for Optimization of Safety-Critical Systems: The ARTEO Algorithm
论文作者
论文摘要
我们认为在具有安全限制的环境中，在不确定性下的决策问题。许多业务和工业应用都依赖实时优化来改善关键绩效指标。在未知特征的情况下，实时优化变得具有挑战性，尤其是由于安全限制的满意度。我们提出了ARTEO算法，在该算法中，我们将多武器的土匪作为数学编程问题施放，但要受安全约束，并通过探索来学习未知的特征，同时优化目标。我们通过使用高斯工艺来量化未知特征的不确定性，并将其纳入成本函数，作为推动探索的贡献。我们根据环境要求适应地控制此贡献的规模。我们通过在高斯过程的规律性假设下构建的置信界来确保算法的安全性具有很高的可能性。我们通过两个案例研究证明了方法的安全性和效率：电动机电流和实时投标问题的优化。我们进一步评估了ARTEO的性能与上置信度结合算法的安全变体相比。 Arteo通过准确且安全的决策获得了累积的后悔。
We consider the problem of decision-making under uncertainty in an environment with safety constraints. Many business and industrial applications rely on real-time optimization to improve key performance indicators. In the case of unknown characteristics, real-time optimization becomes challenging, particularly because of the satisfaction of safety constraints. We propose the ARTEO algorithm, where we cast multi-armed bandits as a mathematical programming problem subject to safety constraints and learn the unknown characteristics through exploration while optimizing the targets. We quantify the uncertainty in unknown characteristics by using Gaussian processes and incorporate it into the cost function as a contribution which drives exploration. We adaptively control the size of this contribution in accordance with the requirements of the environment. We guarantee the safety of our algorithm with a high probability through confidence bounds constructed under the regularity assumptions of Gaussian processes. We demonstrate the safety and efficiency of our approach with two case studies: optimization of electric motor current and real-time bidding problems. We further evaluate the performance of ARTEO compared to a safe variant of upper confidence bound based algorithms. ARTEO achieves less cumulative regret with accurate and safe decisions.
