摘要翻译：
我们研究了POMDPs的一个子类，称为确定性POMDPs，它以确定性的行为和观测为特征。这些模型并没有提供POMDPs的相同通用性，但它们捕获了许多有趣和具有挑战性的问题，并允许更有效的算法。事实上，最近在规划方面的一些工作是围绕这些假设建立的，主要是通过寻求比经典确定性模型更有表现力的可服从模型。我们给出了关于确定性POMDPs的基本性质，它们与和/或搜索问题和算法的关系，以及它们的计算复杂度的结果。
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英文标题：
《Deterministic POMDPs Revisited》
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作者：
Blai Bonet
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最新提交年份：
2012
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  We study a subclass of POMDPs, called Deterministic POMDPs, that is characterized by deterministic actions and observations. These models do not provide the same generality of POMDPs yet they capture a number of interesting and challenging problems, and permit more efficient algorithms. Indeed, some of the recent work in planning is built around such assumptions mainly by the quest of amenable models more expressive than the classical deterministic models. We provide results about the fundamental properties of Deterministic POMDPs, their relation with AND/OR search problems and algorithms, and their computational complexity.
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PDF链接：
https://arxiv.org/pdf/1205.2659