Computing LPMLN using ASP and MLN solvers*
Computing LPMLN using ASP and MLN solvers*
复制标题
使用 ASP 和 MLN 求解器计算 LPMLN*
DOI：
10.1017/s1471068417000400
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发表时间：
2017
影响因子：
1.4
通讯作者：
Yi Wang
中科院分区：
文献类型：
--
作者：
Joohyung Lee;Samidh Talsania;Yi Wang
摘要 LPMLN 是概率逻辑编程语言的最新成员。其主要思想是通过类似于马尔可夫逻辑定义的方式为每个规则分配权重来克服稳定模型语义的刚性。我们提出了 LPMLN 的两个实现：lpmln2asp 和 lpmln2mln。系统lpmln2asp将LPMLN程序翻译成答案集求解器clingo的输入语言，并使用弱约束和稳定模型枚举，它可以计算最可能的稳定模型以及精确的条件概率和边际概率。系统 lpmln2mln 将 LPMLN 程序翻译为马尔可夫逻辑求解器的输入语言，例如 alchemy、tuffy 和 rockit，并允许对 LPMLN 程序执行近似概率推理。我们还演示了 LPMLN 系统用于计算其他语言的有用性，例如 ProbLog 和 Pearl 的因果模型，这些模型被证明可以转换为 LPMLN。
Abstract LPMLN is a recent addition to probabilistic logic programming languages. Its main idea is to overcome the rigid nature of the stable model semantics by assigning a weight to each rule in a way similar to Markov Logic is defined. We present two implementations of LPMLN, lpmln2asp and lpmln2mln. System lpmln2asp translates LPMLN programs into the input language of answer set solver clingo, and using weak constraints and stable model enumeration, it can compute most probable stable models as well as exact conditional and marginal probabilities. System lpmln2mln translates LPMLN programs into the input language of Markov Logic solvers, such as alchemy, tuffy, and rockit, and allows for performing approximate probabilistic inference on LPMLN programs. We also demonstrate the usefulness of the LPMLN systems for computing other languages, such as ProbLog and Pearl's Causal Models, that are shown to be translatable into LPMLN.