We consider the problem of recovering a latent graph where the observations at each node are \emph{aliased}, and transitions are stochastic. Observations are gathered by an agent traversing the graph. Aliasing means that multiple nodes emit the same observation, so the agent can not know in which node it is located. The agent needs to uncover the hidden topology as accurately as possible and in as few steps as possible. This is equivalent to efficient recovery of the transition probabilities of a partially observable Markov decision process (POMDP) in which the observation probabilities are known. An algorithm for efficiently exploring (and ultimately recovering) the latent graph is provided. Our approach is exponentially faster than naive exploration in a variety of challenging topologies with aliased observations while remaining competitive with existing baselines in the unaliased regime.

翻译：我们研究了在节点观测存在“含混”（aliased）且转移过程具有随机性的情况下恢复潜在图的问题。观测数据由遍历该图的智能体收集。含混意味着多个节点发出相同的观测值，因此智能体无法确定自身所处的节点。智能体需要尽可能准确地并以最少步数揭示隐藏的拓扑结构。这等价于高效恢复部分可观测马尔可夫决策过程（POMDP）中的转移概率，其中观测概率已知。本文提供了一种用于高效探索（并最终恢复）潜在图的算法。在多种具有含混观测的挑战性拓扑结构中，我们的方法比朴素探索快指数级，同时在无含混场景下与现有基线方法保持竞争力。