概率逻辑编程系统Fusemate中的自底向上实例化（Grounding）
Bottom-Up Grounding in the Probabilistic Logic Programming System Fusemate
AI总结：
本文介绍概率逻辑编程系统Fusemate，其采用自底向上实例化结合查询引导相关性测试控制基例规模，实验显示其性能可比肩甚至优于现有先进系统，高分支问题上优势明显。
摘要：
本文介绍了概率逻辑编程系统Fusemate。Fusemate的推理引擎由一个实例化组件和一种用于概率推理的变量消元方法组成。Fusemate与大多数其他系统的不同之处在于，它以自底向上的方式对程序进行实例化，而非常见的自顶向下方式。尽管自底向上实例化因诸多原因颇具吸引力，例如可动态生成支持大小各异的分布，但这也使得控制生成的基例子句数量变得更难。我们通过将实例化与查询引导的相关性测试交错执行来解决该问题，该测试会剪枝那些规则体与查询不一致的规则。我们详细阐述了所提方法，并通过涉及“时间”的示例（如（隐）马尔可夫模型）对其进行了演示。我们的实验表明，与当前最先进的概率逻辑编程系统相比，Fusemate性能具有竞争力甚至更优，尤其在高分支问题上表现突出。
英文摘要：
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.
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