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.

部分可观察马尔可夫决策过程 (POMDP) 是在部分可观察性和随机行为下进行决策的有用模型。本文将 POMDP 建模为随机条件规划问题，并提出了两种领域独立的启发式算法，一种基于经典规划中著名的 h_add 启发式算法，另一种在信念空间中计算并考虑信息价值。