Probabilistic logic programming for hybrid relational domains
Probabilistic logic programming for hybrid relational domains
复制标题
混合关系域的概率逻辑编程
DOI：
10.1007/s10994-016-5558-8
复制
发表时间：
2016
期刊：
影响因子：
7.5
通讯作者：
Luc de Raedt
中科院分区：
文献类型：
--
作者：
D. Nitti;T. De Laet;Luc de Raedt
介绍了一种基于分布子句的概率语言和一种高效的混合关系域静态和动态推理算法。静态推理基于抽样，其中样本表示（部分）世界（具有离散和连续变量）。此外，我们使用反向推理来确定哪些事实应该包含在部分世界中。对于动态模型的过滤，我们将静态推理算法与粒子滤波器相结合，并保证之前的部分样本可以安全地遗忘，这在大多数逻辑过滤框架中是不成立的。实验表明，该框架在静态和动态推理方面优于经典的采样方法，在机器人和视觉应用方面具有广阔的应用前景。此外，它在大多数概率编程语言失败的领域提供了正确的结果。
We introduce a probabilistic language and an efficient inference algorithm based on distributional clauses for static and dynamic inference in hybrid relational domains. Static inference is based on sampling, where the samples represent (partial) worlds (with discrete and continuous variables). Furthermore, we use backward reasoning to determine which facts should be included in the partial worlds. For filtering in dynamic models we combine the static inference algorithm with particle filters and guarantee that the previous partial samples can be safely forgotten, a condition that does not hold in most logical filtering frameworks. Experiments show that the proposed framework can outperform classic sampling methods for static and dynamic inference and that it is promising for robotics and vision applications. In addition, it provides the correct results in domains in which most probabilistic programming languages fail.