This paper introduces the Fusemate probabilistic logic programming system. Fusemate's inference engine comprises a grounding component and a variable elimination method for probabilistic inference. Fusemate differs from most other systems by grounding the program in a bottom-up way instead of the common top-down way. While bottom-up grounding is attractive for a number of reasons, e.g., for dynamically creating distributions of varying support sizes, it makes it harder to control the amount of ground clauses generated. We address this problem by interleaving grounding with a query-guided relevance test which prunes rules whose bodies are inconsistent with the query. % This is done We present our method in detail and demonstrate it with examples that involve "time", such as (hidden) Markov models. Our experiments demonstrate competitive or better performance compared to a state-of-the art probabilistic logic programming system, in particular for high branching problems.

翻译：本文介绍Fusemate概率逻辑编程系统。Fusemate的推理引擎包含一个基础化组件和用于概率推理的变量消去方法。与大多数采用常见自顶向下方式的系统不同，Fusemate采用自底向上的方式对程序进行基础化。尽管自底向上基础化因多种优势而具有吸引力（例如可动态生成可变支撑规模的分布），但这也增加了对生成子句数量的控制难度。我们通过将基础化过程与查询导向的相关性测试相结合来解决该问题——该测试可剪除体部与查询不一致的规则。本文详细阐述了该方法，并通过涉及"时间"因素的示例（如隐马尔可夫模型）进行演示。实验表明，与当前最先进的概率逻辑编程系统相比，我们的方法在整体性能上具有竞争力或更优表现，尤其在高分支问题上优势显著。