経路依存型の情報獲得のための変分フレームワーク
A Path-Dependent Variational Framework for Incremental Information Gathering
経路に沿った情報獲得が履歴依存かつ劣モジュラ的である問題に対し、記憶付きラグランジアンの一次最適性条件を導出する枠組みを提案した論文。
著者: William Clark, Maani Ghaffari
分類: cs.RO, math.OC
原文アブストラクト
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.
