Optimizing Value of Information Over an Infinite Time Horizon Decisionmaking based on probabilistic reasoning often involves selecting a subset of expensive observations that best predict the system state. In an earlier work, adopting the general notion of value of information (VoI) first introduced by Krause and Guestrin, Ghosh and Ramakrishnan considered the problem of determining optimal conditional observation plans in temporal graphical models, based on nonmyopic (nongreedy) VoI, over a finite time horizon. They cast the problem as determining optimal policies in finitehorizon, nondiscounted Markov Decision Processes (MDPs). However, there are many practical scenarios where a time horizon is undefinable. In this paper, we consider the VoI optimization problem over an infinite (or equivalently, undefined) time horizon. Adopting an approach similar to Ghosh and Ramakrishnanu0027s, we cast this problem as determining optimal policies in infinitehorizon, finitestate, discounted MDPs. Although our MDPbased framework addresses Dynamic Bayesian Networks (DBNs) that are more restricted than those addressed by Ghosh and Ramakrishnan, we incorporate Krause and Guestrinu0027s general idea of VoI even though it was fundamentally envisioned for finitehorizon settings. We establish the utility of our approach on two graphical models based on realworld datasets.

Maximum Information Bounds for Planning Active Sensing Trajectories This paper considers the problem of planning trajectories for robots equipped with sensors whose task is to track an evolving target process in the world. We focus on processes which can be represented by a Gaussian random variable, which is known to reduce the general stochastic information acquisition problem to a deterministic problem, which is much simpler to solve. Previous work on solving the resulting deterministic problem focuses on computing a search tree by Forward Value Iteration and pruning uninformative nodes early on in the search via a domination criteria. In this work we formulate the Active Information Acquisition problem as a deterministic planning problem where algorithms like Dijkstra and mathrmA* can produce optimal solutions. To use mathrmA* effectively in long planning horizons we derive a consistent and admissible heuristic as a function of the sensor model which can be used in information acquisition tasks such as actively mapping static and moving targets in an environment with obstacles. We validate the results in several simulations indicating that the resulting heuristic informed algorithm can recover optimal solutions faster than existing searchbased methods.