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.

Plingo 是基于 ASP 的 LP^MLN 的概率扩展，并可以映射到优化问题，提供了三种使用不同输入语言和推理模式的前端。通过基于新的方法的近似技术，将 LP^MLN 重新实现为现代 ASP 技术的核心。我们通过与其他概率系统进行比较，从经验上评估 Plingo 的性能。