论文标题
一个依赖路径的变异框架，用于收集的增量信息
A Path-Dependent Variational Framework for Incremental Information Gathering
论文作者
论文摘要
沿着路径收集的信息本质上是下次的。由于冗余观测值，沿路径获得的增量量减少。除了分二次，获得的信息不仅是当前状态的函数，而且是整个历史记录的函数。本文介绍了记忆（依赖历史）拉格朗日的一阶必要最佳条件的构建。与路径相关的问题经常出现在机器人技术和人工智能中，其中诸如地图之类的状态是可以观察到的，并且只能通过局部传感沿轨迹获得信息。机器人探索和环境监测具有许多现实世界应用，可以使用拟议的方法来配制。
Information gathered along a path is inherently submodular; the incremental amount of information gained along a path decreases due to redundant observations. In addition to submodularity, the incremental amount of information gained is a function of not only the current state but also the entire history as well. This paper presents the construction of the first-order necessary optimality conditions for memory (history-dependent) Lagrangians. Path-dependent problems frequently appear in robotics and artificial intelligence, where the state such as a map is partially observable, and information can only be obtained along a trajectory by local sensing. Robotic exploration and environmental monitoring has numerous real-world applications and can be formulated using the proposed approach.
