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编程框架中仍是一项重要挑战，这主要源于此类网络的复杂结构。本文提出probLO（概率线性对象）——一种对Andreoli与Pareschi的LO语言的扩展，该语言将贝叶斯网络的表示与计算嵌入到乘加性线性逻辑编程框架中。其核心创新在于采用类Prolog多头部方法重构非必然树状的网络结构，并结合线性逻辑文献中的标准切片操作，实现了不依赖外部语义解释的内部数值概率计算。