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. 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：本文介绍了Fusemate概率逻辑编程系统。Fusemate的推理引擎包含基化组件和用于概率推理的变量消元方法。与大多数其他系统不同，Fusemate采用自底向上而非常见的自顶向下的方式对程序进行基化。尽管自底向上基化因诸多原因（例如动态创建不同支持规模的分布）具有吸引力，但它使得控制生成的地面子句数量变得更加困难。我们通过将基化与查询引导的相关性测试交替进行来解决这一问题，该测试会剪除体部与查询不一致的规则。我们详细介绍了该方法，并通过涉及"时间"的示例（如（隐）马尔可夫模型）进行了演示。实验结果表明，与最先进的概率逻辑编程系统相比，我们的方法在性能上具有竞争力或更优，尤其是在高分支问题上表现突出。