利用强化学习管理流推理的缓存策略
Managing caching strategies for stream reasoning with reinforcement learning
摘要：
对不断变化的数据进行有效的决策对于许多应用领域都是必不可少的，例如网络物理系统，工业数字化等。现代流推理框架允许人们在新数据到达流时使用增量和连续评估程序来建模和解决各种现实问题。应用技术使用，例如，类似数据记录的实体化或真值维护算法，以避免昂贵的重新计算，从而确保流推理器的低延迟和高吞吐量。然而，现有方法的表现力是相当有限的，例如，它们不能用于编码在实践中经常出现的具有约束的问题。在本文中，我们提出了一种新的方法，使用的约束驱动的约束学习（CDCL），有效地更新遗留的解决方案，通过使用智能管理的学习约束。特别是，我们研究了强化学习的适用性，以不断评估在以前调用的求解算法为当前计算的学习约束的效用。对现实世界的重构问题进行的评估表明，提供一个CDCL算法与相关的学习约束，从以前的迭代结果显着的性能改进的算法在流推理的情况下。正在考虑接受TPLP。
英文摘要：
Efficient decision-making over continuously changing data is essential for many application domains such as cyber-physical systems, industry digitalization, etc. Modern stream reasoning frameworks allow one to model and solve various real-world problems using incremental and continuous evaluation of programs as new data arrives in the stream. Applied techniques use, e.g., Datalog-like materialization or truth maintenance algorithms to avoid costly re-computations, thus ensuring low latency and high throughput of a stream reasoner. However, the expressiveness of existing approaches is quite limited and, e.g., they cannot be used to encode problems with constraints, which often appear in practice. In this paper, we suggest a novel approach that uses the Conflict-Driven Constraint Learning (CDCL) to efficiently update legacy solutions by using intelligent management of learned constraints. In particular, we study the applicability of reinforcement learning to continuously assess the utility of learned constraints computed in previous invocations of the solving algorithm for the current one. Evaluations conducted on real-world reconfiguration problems show that providing a CDCL algorithm with relevant learned constraints from previous iterations results in significant performance improvements of the algorithm in stream reasoning scenarios. Under consideration for acceptance in TPLP.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
