Current methods for solving Stochastic Shortest Path Problems (SSPs) find states' costs-to-go by applying Bellman backups, where state-of-the-art methods employ heuristics to select states to back up and prune. A fundamental limitation of these algorithms is their need to compute the cost-to-go for every applicable action during each state backup, leading to unnecessary computation for actions identified as sub-optimal. We present new connections between planning and operations research and, using this framework, we address this issue of unnecessary computation by introducing an efficient version of constraint generation for SSPs. This technique allows algorithms to ignore sub-optimal actions and avoid computing their costs-to-go. We also apply our novel technique to iLAO* resulting in a new algorithm, CG-iLAO*. Our experiments show that CG-iLAO* ignores up to 57% of iLAO*'s actions and it solves problems up to 8x and 3x faster than LRTDP and iLAO*.

翻译：当前解决随机最短路径问题（SSPs）的方法通过应用贝尔曼备份来计算状态的目标成本，其中最新方法采用启发式策略选择备份和剪枝的状态。这些算法的一个根本局限性在于，每次状态备份时需要计算每个可执行动作的目标成本，导致对已识别为次优的动作产生不必要的计算。我们提出了规划与运筹学之间的新联系，并基于此框架，通过引入一种高效的SSP约束生成技术来解决这一不必要计算问题。该技术允许算法忽略次优动作并避免计算其目标成本。同时，我们将这一创新技术应用于iLAO*，从而提出新算法CG-iLAO*。实验表明，CG-iLAO*可忽略iLAO*中高达57%的动作，其求解速度分别比LRTDP和iLAO*快8倍和3倍。