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:论证问题关注于通过它们的关系结构确定一组论点的可接受性。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它。本文的第一个贡献是将概率论证框架解释为概率逻辑程序的一种新颖解释。概率逻辑程序是在某些事实中注释了概率的逻辑程序。我们展示表示概率论证框架的程序不满足概率逻辑编程(PLP)语义中的一个常见假设,即概率事实完全捕获了研究领域中的不确定性。因此,本文的第二个贡献是为一组选择概率事实不能唯一确定逻辑原子真值赋值的程序提供一种新的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的新颖PLP框架。smProbLog支持许多PLP的推理和学习任务,这些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。我们使用试验分析所提出算法的计算成本及其在论证问题数据集中的应用来评估我们的方法。