  In this article, we discuss how to solve information-gathering problems
expressed as rho-POMDPs, an extension of Partially Observable Markov Decision
Processes (POMDPs) whose reward rho depends on the belief state. Point-based
approaches used for solving POMDPs have been extended to solving rho-POMDPs as
belief MDPs when its reward rho is convex in B or when it is
Lipschitz-continuous. In the present paper, we build on the POMCP algorithm to
propose a Monte Carlo Tree Search for rho-POMDPs, aiming for an efficient
on-line planner which can be used for any rho function. Adaptations are
required due to the belief-dependent rewards to (i) propagate more than one
state at a time, and (ii) prevent biases in value estimates. An asymptotic
convergence proof to epsilon-optimal values is given when rho is continuous.
Experiments are conducted to analyze the algorithms at hand and show that they
outperform myopic approaches.
