Grounding Rule-Based Argumentation Using Datalog

• URL: http://arxiv.org/abs/2508.10976v1
• Date: Thu, 14 Aug 2025 17:57:32 GMT
• Title: Grounding Rule-Based Argumentation Using Datalog
• Abstract summary: ASPIC+ is one of the main general frameworks for rule-based argumentation for AI.<n>We propose an intelligent grounding procedure that keeps the size of the grounding manageable while preserving the correctness of the reasoning process.<n>We also propose simplifications specific to the ASPIC+ formalism to avoid grounding of rules that have no influence on the reasoning process.
• Score: 3.1457219084519004
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: ASPIC+ is one of the main general frameworks for rule-based argumentation for AI. Although first-order rules are commonly used in ASPIC+ examples, most existing approaches to reason over rule-based argumentation only support propositional rules. To enable reasoning over first-order instances, a preliminary grounding step is required. As groundings can lead to an exponential increase in the size of the input theories, intelligent procedures are needed. However, there is a lack of dedicated solutions for ASPIC+. Therefore, we propose an intelligent grounding procedure that keeps the size of the grounding manageable while preserving the correctness of the reasoning process. To this end, we translate the first-order ASPIC+ instance into a Datalog program and query a Datalog engine to obtain ground substitutions to perform the grounding of rules and contraries. Additionally, we propose simplifications specific to the ASPIC+ formalism to avoid grounding of rules that have no influence on the reasoning process. Finally, we performed an empirical evaluation of a prototypical implementation to show scalability.

Related papers

• DAR: Deontic Reasoning with Agentic Harnesses [48.798309189058614]
  We introduce Deontic Agentic Reasoning (DAR), an agentic reasoning setup in which the model interacts with the statutes on demand.<n>We evaluate DAR under multiple harnesses on hard subsets of DeonticBench.
  arXiv  Detail & Related papers  (2026-06-03T15:29:38Z)
• DeonticBench: A Benchmark for Reasoning over Rules [52.69517904415795]
  DEONTICBENCH is a benchmark of 6,232 tasks across U.S. federal taxes, airline baggage policies, U.S. immigration administration, and U.S. state housing law.<n>It is a benchmark for studying context-grounded rule reasoning in real-world domains under both symbolic and non-symbolic settings.
  arXiv  Detail & Related papers  (2026-04-06T05:41:02Z)
• Reasoning Core: A Scalable Procedural Data Generation Suite for Symbolic Pre-training and Post-Training [2.62112541805429]
  Reasoning Core is a scalable suite that procedurally generates verifiable symbolic reasoning data across core formal domains.<n>Each task is paired with an external solver for rigorous verification and admits continuous difficulty control for curriculum design.<n>Experiments show that mixing Reasoning Core data into pre-training improves downstream reasoning while preserving, or slightly improving, language modeling quality.
  arXiv  Detail & Related papers  (2026-03-02T18:59:29Z)
• Do It for HER: First-Order Temporal Logic Reward Specification in Reinforcement Learning (Extended Version) [49.462399222747024]
  We propose a novel framework for the logical specification of non-Markovian rewards in Decision Processes (MDPs) with large state spaces.<n>Our approach leverages Linear Temporal Logic Modulo Theories over finite traces (LTLfMT)<n>We introduce a method based on reward machines and Hindsight Experience Replay (HER) to translate first-order logic specifications and address reward sparsity.
  arXiv  Detail & Related papers  (2026-02-05T22:11:28Z)
• Symbolic Working Memory Enhances Language Models for Complex Rule Application [87.34281749422756]
  Large Language Models (LLMs) have shown remarkable reasoning performance but struggle with multi-step deductive reasoning. We propose augmenting LLMs with external working memory and introduce a neurosymbolic framework for rule application. Our framework iteratively performs symbolic rule grounding and LLM-based rule implementation.
  arXiv  Detail & Related papers  (2024-08-24T19:11:54Z)
• Logicbreaks: A Framework for Understanding Subversion of Rule-based Inference [20.057611113206324]
  We formalize rule-following as inference in propositional Horn logic.<n>We prove that although small transformers can faithfully follow such rules, maliciously crafted prompts can still mislead both theoretical constructions and models learned from data.
  arXiv  Detail & Related papers  (2024-06-21T19:18:16Z)
• Disperse-Then-Merge: Pushing the Limits of Instruction Tuning via Alignment Tax Reduction [75.25114727856861]
  Large language models (LLMs) tend to suffer from deterioration at the latter stage ofSupervised fine-tuning process. We introduce a simple disperse-then-merge framework to address the issue. Our framework outperforms various sophisticated methods such as data curation and training regularization on a series of standard knowledge and reasoning benchmarks.
  arXiv  Detail & Related papers  (2024-05-22T08:18:19Z)
• Can LLMs Reason with Rules? Logic Scaffolding for Stress-Testing and Improving LLMs [87.34281749422756]
  Large language models (LLMs) have achieved impressive human-like performance across various reasoning tasks. However, their mastery of underlying inferential rules still falls short of human capabilities. We propose a logic scaffolding inferential rule generation framework, to construct an inferential rule base, ULogic.
  arXiv  Detail & Related papers  (2024-02-18T03:38:51Z)
• Bottom-Up Grounding in the Probabilistic Logic Programming System Fusemate [0.21756081703275998]
  Fusemate's inference engine comprises a grounding component and a variable elimination method for probabilistic inference. We present our method in detail and demonstrate it with examples that involve "time", such as (hidden) Markov models.
  arXiv  Detail & Related papers  (2023-05-30T10:30:44Z)
• Learning Symbolic Rules for Reasoning in Quasi-Natural Language [74.96601852906328]
  We build a rule-based system that can reason with natural language input but without the manual construction of rules. We propose MetaQNL, a "Quasi-Natural" language that can express both formal logic and natural language sentences. Our approach achieves state-of-the-art accuracy on multiple reasoning benchmarks.
  arXiv  Detail & Related papers  (2021-11-23T17:49:00Z)
• RNNLogic: Learning Logic Rules for Reasoning on Knowledge Graphs [91.71504177786792]
  This paper studies learning logic rules for reasoning on knowledge graphs. Logic rules provide interpretable explanations when used for prediction as well as being able to generalize to other tasks. Existing methods either suffer from the problem of searching in a large search space or ineffective optimization due to sparse rewards.
  arXiv  Detail & Related papers  (2020-10-08T14:47:02Z)
• Stratified Rule-Aware Network for Abstract Visual Reasoning [46.015682319351676]
  Raven's Progressive Matrices (RPM) test is typically used to examine the capability of abstract reasoning. Recent studies, taking advantage of Convolutional Neural Networks (CNNs), have achieved encouraging progress to accomplish the RPM test. We propose a Stratified Rule-Aware Network (SRAN) to generate the rule embeddings for two input sequences.
  arXiv  Detail & Related papers  (2020-02-17T08:44:05Z)

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.