摘要翻译：
动态系统中的近似推理是在给定一系列动作和部分观测的情况下估计系统状态的问题。高精度估计是诊断、自然语言处理、跟踪、规划和机器人等许多应用的基础。本文提出了一种对概率序列中可能的确定性执行进行采样的算法。该算法利用动作和世界状态的紧凑表示（使用一阶逻辑）来提高估计精度。理论和实验结果表明，该算法的期望误差小于命题采样和序贯蒙特卡罗(SMC)采样技术。
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英文标题：
《Sampling First Order Logical Particles》
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作者：
Hannaneh Hajishirzi, Eyal Amir
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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：
  Approximate inference in dynamic systems is the problem of estimating the state of the system given a sequence of actions and partial observations. High precision estimation is fundamental in many applications like diagnosis, natural language processing, tracking, planning, and robotics. In this paper we present an algorithm that samples possible deterministic executions of a probabilistic sequence. The algorithm takes advantage of a compact representation (using first order logic) for actions and world states to improve the precision of its estimation. Theoretical and empirical results show that the algorithm's expected error is smaller than propositional sampling and Sequential Monte Carlo (SMC) sampling techniques.
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PDF链接：
https://arxiv.org/pdf/1206.3264