Logic Programming Semantics for Causal Processes
- URL: http://arxiv.org/abs/2607.21233v1
- Date: Thu, 23 Jul 2026 11:48:45 GMT
- Title: Logic Programming Semantics for Causal Processes
- Abstract summary: We investigate the relationship between logic programming semantics and the eventual states of causal processes compatible with those logic programs.<n>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.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
Related papers
- SALA: Semantic-Aware Logical Alignment for Complex Reasoning in In-Context Learning [61.13104919370034]
We propose SALA, a Semantic-Aware Logical Alignment framework.<n>Instead of relying on a fixed inventory, SALA automatically learns task-specific reasoning operations.<n>It then embeds these operations into a continuous semantic space and uses dynamic time warping to align the reasoning sequences.
arXiv Detail & Related papers (2026-09-02T09:12:03Z) - How Rules Represent Causal Knowledge: Causal Modeling with Probabilistic Logic Programming [0.0]
This paper brings Pearl's approach to causality into probabilistic logic programming (PLP)<n>A formal causal semantics for these programs, together with a notion of intervention and an implementation, is proposed.
arXiv Detail & Related papers (2026-07-23T11:21:39Z) - On the Trap Space Semantics of Normal Logic Programs [2.274915755738124]
We introduce trap space semantics to arbitrary normal logic programs.<n>This new semantics admits both model-theoretic and dynamical characterizations.<n>Our results demonstrate that the trap space semantics offers a unified and precise framework for proving the existence of supported classes.
arXiv Detail & Related papers (2026-01-07T12:06:26Z) - Schoenfeld's Anatomy of Mathematical Reasoning by Language Models [56.656180566692946]
We adopt Schoenfeld's Episode Theory as an inductive, intermediate-scale lens and introduce ThinkARM (Anatomy of Reasoning in Models)<n>ThinkARM explicitly abstracts reasoning traces into functional reasoning steps such as Analysis, Explore, Implement, verify, etc.<n>We show that episode-level representations make reasoning steps explicit, enabling systematic analysis of how reasoning is structured, stabilized, and altered in modern language models.
arXiv Detail & Related papers (2025-12-23T02:44:25Z) - Are Language Models Efficient Reasoners? A Perspective from Logic Programming [109.47572890883248]
Modern language models (LMs) exhibit strong deductive reasoning capabilities, yet standard evaluations emphasize correctness while overlooking a key aspect of human-like reasoning: efficiency.<n>We propose a framework for assessing LM reasoning efficiency through the lens of logic programming.
arXiv Detail & Related papers (2025-10-29T15:30:31Z) - Explainable Chain-of-Thought Reasoning: An Empirical Analysis on State-Aware Reasoning Dynamics [69.00587226225232]
We introduce a state-aware transition framework that abstracts CoT trajectories into structured latent dynamics.<n>To characterize the global structure of reasoning, we model their progression as a Markov chain.<n>This abstraction supports a range of analyses, including semantic role identification, temporal pattern visualization, and consistency evaluation.
arXiv Detail & Related papers (2025-08-29T18:53:31Z) - How Rules Represent Causal Knowledge: Causal Modeling with Abductive Logic Programs [0.0]
This paper extends Pearl's approach to causality and interventions to the setting of stratified abductive logic programs.<n>It shows how stable models of such programs can be given a causal interpretation by building on philosophical foundations.
arXiv Detail & Related papers (2025-07-07T15:12:01Z) - On the Logical Content of Logic Programs [0.0]
This paper introduces a novel perspective on Logic programming (LP) by defining a support'' relation that explicates what a program knows''<n>Results are formalized using the idea of base-extension semantics within proof-theoretic semantics.<n>Our approach offers new insights into the logical foundations of LP and has potential applications in knowledge representation, automated reasoning, and formal verification.
arXiv Detail & Related papers (2025-03-07T11:58:08Z) - Temporal Causal Reasoning with (Non-Recursive) Structural Equation Models [9.112107794815671]
We propose a new interpretation of Structural Equation Models (SEMs) when reasoning about Actual Causality.<n>This allows us to combine counterfactual causal reasoning with existing temporal logic formalisms.<n>We show that the standard restriction to so-called textitrecursive models is not necessary in our approach.
arXiv Detail & Related papers (2025-01-17T13:37:58Z) - Modeling Hierarchical Reasoning Chains by Linking Discourse Units and
Key Phrases for Reading Comprehension [80.99865844249106]
We propose a holistic graph network (HGN) which deals with context at both discourse level and word level, as the basis for logical reasoning.
Specifically, node-level and type-level relations, which can be interpreted as bridges in the reasoning process, are modeled by a hierarchical interaction mechanism.
arXiv Detail & Related papers (2023-06-21T07:34:27Z) - MetaLogic: Logical Reasoning Explanations with Fine-Grained Structure [129.8481568648651]
We propose a benchmark to investigate models' logical reasoning capabilities in complex real-life scenarios.
Based on the multi-hop chain of reasoning, the explanation form includes three main components.
We evaluate the current best models' performance on this new explanation form.
arXiv Detail & Related papers (2022-10-22T16:01:13Z) - Discourse-Aware Graph Networks for Textual Logical Reasoning [142.0097357999134]
Passage-level logical relations represent entailment or contradiction between propositional units (e.g., a concluding sentence)
We propose logic structural-constraint modeling to solve the logical reasoning QA and introduce discourse-aware graph networks (DAGNs)
The networks first construct logic graphs leveraging in-line discourse connectives and generic logic theories, then learn logic representations by end-to-end evolving the logic relations with an edge-reasoning mechanism and updating the graph features.
arXiv Detail & Related papers (2022-07-04T14:38:49Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.