论文标题
基于UCB的树木搜索方法，用于大规模系统的联合验证校正策略
A UCB-based Tree Search Approach to Joint Verification-Correction Strategy for Large Scale Systems
论文作者
论文摘要
验证计划是一个顺序决策问题，指定了系统开发不同阶段的一组验证活动（VA）和校正活动（CA）。虽然VAS用于识别错误和缺陷，但CAS在校正已确定的错误和缺陷时在系统验证中也起着重要作用。但是，当前的计划方法仅将VA视为决策选择。由于VAS和CAS具有不同的活动空间，因此计划联合验证校正策略（JVC）仍然具有挑战性，尤其是对于大型系统。在这里，我们介绍了一种基于UCB的树搜索方法，以搜索近乎最佳的JVCSS。首先，验证计划被简化为可重复的匪徒问题，并以最佳的遗憾结合给出了可重复强盗（UCBRB）的上限置信界规则（UCBRB）。接下来，提出了树搜索算法来搜索可行的JVCSS。基于树的合奏学习模型还用于扩展树搜索算法以处理本地最佳问题。在通信系统的概念案例中评估了所提出的方法。
Verification planning is a sequential decision-making problem that specifies a set of verification activities (VA) and correction activities (CA) at different phases of system development. While VAs are used to identify errors and defects, CAs also play important roles in system verification as they correct the identified errors and defects. However, current planning methods only consider VAs as decision choices. Because VAs and CAs have different activity spaces, planning a joint verification-correction strategy (JVCS) is still challenging, especially for large-size systems. Here we introduce a UCB-based tree search approach to search for near-optimal JVCSs. First, verification planning is simplified as repeatable bandit problems and an upper confidence bound rule for repeatable bandits (UCBRB) is presented with the optimal regret bound. Next, a tree search algorithm is proposed to search for feasible JVCSs. A tree-based ensemble learning model is also used to extend the tree search algorithm to handle local optimality issues. The proposed approach is evaluated on the notional case of a communication system.
