Partially Observable Markov Decision Processes (POMDPs) provide a rich
framework for sequential decision-making under uncertainty in stochastic
domains. However, solving a POMDP is often intractable except for small
problems due to their complexity. Here, we focus on online approaches that
alleviate the computational complexity by computing good local policies at each
decision step during the execution. Online algorithms generally consist of a
lookahead search to find the best action to execute at each time step in an
environment. Our objectives here are to survey the various existing online
POMDP methods, analyze their properties and discuss their advantages and
disadvantages; and to thoroughly evaluate these online approaches in different
environments under various metrics (return, error bound reduction, lower bound
improvement). Our experimental results indicate that state-of-the-art online
heuristic search methods can handle large POMDP domains efficiently.

本文介绍了一些基于在线方法的局部政策计算的 POMDP 方法，并在各种环境下对这些方法进行了评估，结果表明现代的启发式搜索方法能够高效地处理大型 POMDP 领域。

POMDP 的在线规划算法

Online Planning Algorithms for POMDPs

The intelligent reformulation or restructuring of a belief network can
greatly increase the efficiency of inference. However, time expended for
reformulation is not available for performing inference. Thus, under time
pressure, there is a tradeoff between the time dedicated to reformulating the
network and the time applied to the implementation of a solution. We
investigate this partition of resources into time applied to reformulation and
time used for inference. We shall describe first general principles for
computing the ideal partition of resources under uncertainty. These principles
have applicability to a wide variety of problems that can be divided into
interdependent phases of problem solving. After, we shall present results of
our empirical study of the problem of determining the ideal amount of time to
devote to searching for clusters in belief networks. In this work, we acquired
and made use of probability distributions that characterize (1) the performance
of alternative heuristic search methods for reformulating a network instance
into a set of cliques, and (2) the time for executing inference procedures on
various belief networks. Given a preference model describing the value of a
solution as a function of the delay required for its computation, the system
selects an ideal time to devote to reformulation.

研究在有时间限制的情况下，如何在重构信念网络与实施解决方案之间平衡时间分配；使用启发式搜索方法确定重构时间与执行推理过程时间之间的理想分配，依据偏好模型选择最佳重构时间。