发表机构
Saarland University; Technical University of Denmark; Department of Information Engineering, University of Florence（萨尔兰大学; 丹麦技术大学; 佛罗伦萨大学信息工程系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究时间敏感重要性分裂，通过定时器值重采样和解耦重要性估计与定时器采样，利用模拟全局老化识别修剪不可达执行，形成距离度量分类法，提高了ISPLIT估计器准确性。
AI 中文摘要
重要性分裂（ISPLIT）用于评估非马尔可夫模型中罕见事件的概率，需要启发式重要性函数（IFUN）估计到目标的距离。现有时间敏感IFUN根据单个采样定时器值评估模拟状态，导致许多无效率的模拟运行。本文重新审视时间敏感ISPLIT，旨在引导模拟运行至重要状态。首先研究定时器值如何根据经过时间进行重采样，通过考虑可行定时器值集评估重要性，将重要性估计与定时器采样解耦。其次利用模拟的全局老化识别并修剪在剩余时间预算内无法到达目标的执行。这些想法形成了距离度量分类法，阐明了定时器界限、重采样和全局老化的作用。实验表明，所提出的公式显著提高了ISPLIT估计器的准确性。
英文摘要
Importance splitting (ISPLIT) evaluates the probabilities of rare events in non-Markovian models. It requires a heuristic importance function (IFUN) that estimates the distance to the target. While including timer evaluations in the IFUN can substantially improve the effectiveness of ISPLIT, the existing time-sensitive IFUNs evaluate simulation states with respect to single sampled timer values. Thus, reaching highly important states requires simultaneously sampling specific combinations of timer values, yielding many unproductive simulation runs. In this paper, we revisit time-sensitive ISPLIT with the goal of steering simulation runs towards important states. First, we study how timer values can be resampled conditioned on the elapsed time. The importance can be evaluated by considering the set of feasible timer values, decoupling importance estimation from timer samples. Second, we exploit the global age of a simulation to identify and prune the executions that can no longer reach the target within the remaining time budget. Together, these ideas lead to a taxonomy of distance metrics clarifying the role of timer bounds, resampling, and the global age. In particular, for models with unbounded timers, we show that time-sensitive IFUNs collapse to ordinary IFUNs under resampling. Experiments demonstrate that the proposed formulations substantially improve the accuracy of ISPLIT estimators.
CommentsAccepted at ICTAC 2026 (23rd International Colloquium on Theoretical Aspects of Computing)
