一句话：在随机进程演算中给出因果可逆性与时间可逆性同时成立的条件，并引入双向随机互模拟。
因果可逆性将可逆性与因果性结合，要求一个动作只有在其所有后果都已撤销后才能被撤销，从而把并发系统带回一致的过去状态；时间可逆性则来自随机过程领域，主要用于性能分析，指连续时间马尔可夫链在时间反向后随机行为保持不变。本文旨在桥接这两种可逆性理论，在随机进程演算的框架下研究二者之间的关系。作者在该演算中给出因果可逆性与时间可逆性均由构造保证的条件，并为演算配备一种同时考虑前向与后向行为的随机互模拟变体。结果表明，两类可逆性可以在统一的随机进程代数语义下协调一致。
Causal reversibility blends reversibility and causality for concurrent systems. It indicates that an action can be undone provided that all of its consequences have been undone already, thus making it possible to bring the system back to a past consistent state. Time reversibility is instead considered in the field of stochastic processes, mostly for efficient analysis purposes. A performance model based on a continuous-time Markov chain is time reversible if its stochastic behavior remains the same when the direction of time is reversed. We bridge these two theories of reversibility by showing the conditions under which causal reversibility and time reversibility are both ensured by construction. This is done in the setting of a stochastic process calculus, which is then equipped with a variant of stochastic bisimilarity accounting for both forward and backward directions.
@article{paperbot2220,
title = {Bridging Causal Reversibility and Time Reversibility: A Stochastic Process Algebraic Approach},
author = {Marco Bernardo and Claudio A. Mezzina},
journal = {Logical Methods in Computer Science},
volume = {Volume 19, Issue 2},
year = {2023},
doi = {10.46298/lmcs-19(2:6)2023}
}