摘要翻译：
对动力系统进行建模，既用于控制目的，也用于预测它们的行为，在科学和工程中是普遍存在的。预测状态表示（PSRs）是最近出现的一类用于离散时间动力系统的模型。PSRs和与之密切相关的OOMs（耶格的可观测算子模型）背后的关键思想是将系统的状态表示为一组对系统中可观测实验结果的预测。这使得PSRs与基于历史的模型（如N阶马尔可夫模型）和基于隐藏状态的模型（如HMMs和POMDPS）有很大的不同。我们介绍了一个有趣的构造，系统动力学矩阵，并展示了如何简单地从它导出PSRs。我们还用这个构造形式地证明了PSRs比N阶马尔可夫模型和HMMS/POMDPS更一般。最后，我们讨论了PSRs和OOMs的主要区别，并对今后的工作进行了总结。
---
英文标题：
《Predictive State Representations: A New Theory for Modeling Dynamical
  Systems》
---
作者：
Satinder Singh, Michael James, Matthew Rudary
---
最新提交年份：
2012
---
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。
--
一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
--

---
英文摘要：
  Modeling dynamical systems, both for control purposes and to make predictions about their behavior, is ubiquitous in science and engineering. Predictive state representations (PSRs) are a recently introduced class of models for discrete-time dynamical systems. The key idea behind PSRs and the closely related OOMs (Jaeger's observable operator models) is to represent the state of the system as a set of predictions of observable outcomes of experiments one can do in the system. This makes PSRs rather different from history-based models such as nth-order Markov models and hidden-state-based models such as HMMs and POMDPs. We introduce an interesting construct, the systemdynamics matrix, and show how PSRs can be derived simply from it. We also use this construct to show formally that PSRs are more general than both nth-order Markov models and HMMs/POMDPs. Finally, we discuss the main difference between PSRs and OOMs and conclude with directions for future work.
---
PDF链接：
https://arxiv.org/pdf/1207.4167