Argumentation problems are concerned with determining the acceptability of a set of arguments from their relational structure. When the available information is uncertain, probabilistic argumentation frameworks provide modelling tools to account for it. The first contribution of this paper is a novel interpretation of probabilistic argumentation frameworks as probabilistic logic programs. Probabilistic logic programs are logic programs in which some of the facts are annotated with probabilities. We show that the programs representing probabilistic argumentation frameworks do not satisfy a common assumption in probabilistic logic programming (PLP) semantics, which is, that probabilistic facts fully capture the uncertainty in the domain under investigation. The second contribution of this paper is then a novel PLP semantics for programs where a choice of probabilistic facts does not uniquely determine the truth assignment of the logical atoms. The third contribution of this paper is the implementation of a PLP system supporting this semantics: smProbLog. smProbLog is a novel PLP framework based on the probabilistic logic programming language ProbLog. smProbLog supports many inference and learning tasks typical of PLP, which, together with our first contribution, provide novel reasoning tools for probabilistic argumentation. We evaluate our approach with experiments analyzing the computational cost of the proposed algorithms and their application to a dataset of argumentation problems.
翻译:----
论证问题关注于根据其关系结构确定一组观点的可接受性。当可用信息存在不确定性时,概率论证框架提供建模工具来考虑不确定性。本文第一部分的贡献是将概率论证框架解释为概率逻辑程序。概率逻辑程序是一类在其中一些事实被概率注释的逻辑程序。我们展示了代表概率论证框架的程序不满足概率逻辑程序(PLP)语义的一个普遍假设,即概率事实完全捕捉了调查领域中的不确定性。本文的第二个贡献是针对不同的概率事实选择不能唯一地确定逻辑原子的程序提出了一种新的PLP语义。本文的第三个贡献是支持这种语义的PLP系统的实现:smProbLog。smProbLog是一种基于概率逻辑编程语言ProbLog的新型PLP框架。smProbLog支持许多PLP的典型推理和学习任务,与我们的第一个贡献一起为概率论证提供了新的推理工具。我们通过分析所提出的算法的计算成本及其应用于论证问题数据集的实验来评估我们的方法。