Bayesian networks are a canonical formalism for representing probabilistic dependencies, yet their integration within logic programming frameworks remains a nontrivial challenge, mainly due to the complex structure of these networks. In this paper, we propose probLO (probabilistic Linear Objects) an extension of Andreoli and Pareschi's LO language which embeds Bayesian network representation and computation within the framework of multiplicative-additive linear logic programming. The key novelty is the use of multi-head Prolog-like methods to reconstruct network structures, which are not necessarily trees, and the operation of slicing, standard in the literature of linear logic, enabling internal numerical probability computations without relying on external semantic interpretation.

翻译：贝叶斯网络是表示概率依赖关系的规范形式体系，然而将其整合到逻辑程序设计框架中仍是一个非平凡的挑战，这主要源于这些网络的复杂结构。本文提出probLO（概率线性对象），作为Andreoli与Pareschi的LO语言的扩展，将贝叶斯网络的表示与计算嵌入到乘加性线性逻辑程序设计的框架中。其核心创新在于采用类Prolog多头部方法重构不一定是树结构的网络，并运用线性逻辑文献中的标准切片操作，从而无需依赖外部语义解释即可实现内部数值概率计算。