用逻辑编程语义解析因果过程的最终状态，揭示不同模型的适用场景。
Logic Programming Semantics for Causal Processes
- 用稳定模型和支撑模型区分中性起点与任意起点的因果演化结果。
- 稳定模型对应无限持续且无干扰过程的最终状态，支撑模型覆盖更广起始条件。
- 为逻辑编程作为因果规则语言提供时间维度的解释视角，适合因果建模研究者。
针对生命科学中的复杂建模挑战，本文研究逻辑编程语义与因果过程最终状态之间的关系。具体而言，我们证明：正向逻辑程序的稳定模型对应从初始中性状态出发、持续且不受干扰的过程之最终状态；而支撑模型则描述了从任意起始点可达的最终状态。该工作深化了对逻辑编程语义作为因果规则语言的讨论，从因果解释角度引入时间维度，为理解稳定模型与支撑模型提供了新的视角。
原文摘要 · Abstract (English)
Motivated by challenging modelling issues in the life sciences, we investigate the relationship between logic programming semantics and the eventual states of causal processes compatible with those logic programs. More precisely, we show that while stable models of positive logic programs correspond to the eventual states of processes commencing from a neutral state and continuing undisturbed indefinitely, supported models describe the eventual states reachable from arbitrary starting points. This also contributes to the discussion of the appropriate semantics for logic programming as a causal rule language, adding a temporal perspective to recent interpretations of the stable and supported model semantics from an explanatory viewpoint of causality.
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