论文标题
Plingo：基于LPMLN的克林戈概率推理的系统
plingo: A system for probabilistic reasoning in clingo based on lpmln
论文作者
论文摘要
我们提出Plingo，这是具有各种概率推理模式的ASP系统clingo的扩展。 Plingo以Lp^mln为中心，Lp^mln是基于Markov Logic的权重方案的ASP的概率扩展。这种选择是由于可以将核心概率推理模式映射到优化问题而动机，并且LP^mln可以用作与其他概率方法相关的中间地形式主义。结果，Plingo为Lp^mln，P-Log和Problog提供了三个替代前端。相应的输入语言和推理模式是通过Clingo的多拍和理论解决功能来实现的。 pling脚的核心等于在现代ASP技术方面重新实现LP^mln，并以一种基于新的方法以最佳顺序进行答案集枚举的近似技术扩展。我们通过将Plingo的性能与其他概率系统进行比较，从经验上评估。
We present plingo, an extension of the ASP system clingo with various probabilistic reasoning modes. Plingo is centered upon LP^MLN, a probabilistic extension of ASP based on a weight scheme from Markov Logic. This choice is motivated by the fact that the core probabilistic reasoning modes can be mapped onto optimization problems and that LP^MLN may serve as a middle-ground formalism connecting to other probabilistic approaches. As a result, plingo offers three alternative frontends, for LP^MLN, P-log, and ProbLog. The corresponding input languages and reasoning modes are implemented by means of clingo's multi-shot and theory solving capabilities. The core of plingo amounts to a re-implementation of LP^MLN in terms of modern ASP technology, extended by an approximation technique based on a new method for answer set enumeration in the order of optimality. We evaluate plingo's performance empirically by comparing it to other probabilistic systems.
