  Partially observable Markov decision processes (POMDP) are a useful model for
decision-making under partial observability and stochastic actions. Partially
Observable Monte-Carlo Planning is an online algorithm for deciding on the next
action to perform, using a Monte-Carlo tree search approach, based on the UCT
(UCB applied to trees) algorithm for fully observable Markov-decision
processes. POMCP develops an action-observation tree, and at the leaves, uses a
rollout policy to provide a value estimate for the leaf. As such, POMCP is
highly dependent on the rollout policy to compute good estimates, and hence
identify good actions. Thus, many practitioners who use POMCP are required to
create strong, domain-specific heuristics.
  In this paper, we model POMDPs as stochastic contingent planning problems.
This allows us to leverage domain-independent heuristics that were developed in
the planning community. We suggest two heuristics, the first is based on the
well-known h_add heuristic from classical planning, and the second is computed
in belief space, taking the value of information into account.

Artificial Intelligence