摘要翻译：
不确定性条件下的规划是自动序贯决策研究中的一个核心问题，已被人工智能规划、决策分析、运筹学、控制理论和经济学等多个领域的研究人员所关注。虽然在这些领域所采用的假设和观点往往有很大的不同，但这些领域的研究人员感兴趣的许多规划问题可以被建模为马尔可夫决策过程(MDPs)，并利用决策理论的技术进行分析。本文对MDP相关方法进行了综述和综合，展示了它们如何为人工智能中研究的许多规划问题建模提供一个统一的框架。它还描述了MDPs的结构性质，当特定类别的问题表现出来时，可以在构造最优或近似最优的策略或计划时加以利用。规划问题通常具有用于描述性能标准的奖励和价值函数、用于描述状态转换和观察的函数以及用于描述状态、行动、奖励和观察的特征之间的关系的结构。专门的表示和使用这些表示的算法可以通过利用这些不同形式的结构来实现计算杠杆作用。某些人工智能技术--尤其是那些基于使用结构化、内涵化表示的技术--可以这样看待。本文综述了经典规划问题和决策理论规划问题的几种表示形式，以及利用这些表示形式以多种不同方式来减轻构造策略或计划的计算负担的规划算法。它主要关注基于AI风格表示的抽象、聚合和分解技术。
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英文标题：
《Decision-Theoretic Planning: Structural Assumptions and Computational
  Leverage》
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作者：
C. Boutilier, T. Dean, S. Hanks
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最新提交年份：
2011
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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：
  Planning under uncertainty is a central problem in the study of automated sequential decision making, and has been addressed by researchers in many different fields, including AI planning, decision analysis, operations research, control theory and economics. While the assumptions and perspectives adopted in these areas often differ in substantial ways, many planning problems of interest to researchers in these fields can be modeled as Markov decision processes (MDPs) and analyzed using the techniques of decision theory. This paper presents an overview and synthesis of MDP-related methods, showing how they provide a unifying framework for modeling many classes of planning problems studied in AI. It also describes structural properties of MDPs that, when exhibited by particular classes of problems, can be exploited in the construction of optimal or approximately optimal policies or plans. Planning problems commonly possess structure in the reward and value functions used to describe performance criteria, in the functions used to describe state transitions and observations, and in the relationships among features used to describe states, actions, rewards, and observations. Specialized representations, and algorithms employing these representations, can achieve computational leverage by exploiting these various forms of structure. Certain AI techniques -- in particular those based on the use of structured, intensional representations -- can be viewed in this way. This paper surveys several types of representations for both classical and decision-theoretic planning problems, and planning algorithms that exploit these representations in a number of different ways to ease the computational burden of constructing policies or plans. It focuses primarily on abstraction, aggregation and decomposition techniques based on AI-style representations.
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PDF链接：
https://arxiv.org/pdf/1105.5460