Logic Programming Semantics for Causal Processes

2026-07-23

AI Summary

This study addresses the challenge of providing a semantic interpretation for causal process modeling in life sciences by establishing a correspondence between stable and supported models of logic programs and the terminal states of causal processes. By incorporating a temporal perspective, it interprets stable models for the first time as terminal states of undisturbed causal processes originating from a neutral initial state, whereas supported models characterize all terminal states reachable from arbitrary initial conditions. Integrating semantic theory from logic programming with formal causal modeling, this work offers a novel temporal semantic framework that positions logic programs as a language for causal rules, thereby deepening the understanding of their capacity for causal explanation.

Abstract

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.

Problem

• Logic Programming
• Causal Processes
• Stable Models
• Supported Models
• Semantics

Innovation

• logic programming semantics
• causal processes
• stable models
• supported models
• temporal perspective