Defeasible Conditionals using Answer Set Programming
- URL: http://arxiv.org/abs/2601.03840v1
- Date: Wed, 07 Jan 2026 12:05:41 GMT
- Title: Defeasible Conditionals using Answer Set Programming
- Authors: Racquel Dennison, Jesse Heyninck, Thomas Meyer,
- Abstract summary: This paper presents a declarative definition for computing Rational Closure (RC) using Answer Set Programming (ASP)<n>Our approach enables the automatic construction of the minimal ranked model from a given knowledge base and supports entailment checking for specified queries.
- Score: 10.254243409261901
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Defeasible entailment is concerned with drawing plausible conclusions from incomplete information. A foundational framework for modelling defeasible entailment is the KLM framework. Introduced by Kraus, Lehmann, and Magidor, the KLM framework outlines several key properties for defeasible entailment. One of the most prominent algorithms within this framework is Rational Closure (RC). This paper presents a declarative definition for computing RC using Answer Set Programming (ASP). Our approach enables the automatic construction of the minimal ranked model from a given knowledge base and supports entailment checking for specified queries. We formally prove the correctness of our ASP encoding and conduct empirical evaluations to compare the performance of our implementation with that of existing imperative implementations, specifically the InfOCF solver. The results demonstrate that our ASP-based approach adheres to RC's theoretical foundations and offers improved computational efficiency.
Related papers
- Structure-Aware Robust Counterfactual Explanations via Conditional Gaussian Network Classifiers [0.26999000177990923]
This work presents a structure-aware robustness-and-counterfactual search method based on conditional conditional graphs.<n>Results show that our method achieves strong consistency, with direct optimization of the original formulation providing especially stable dependencies.<n>The proposed framework lays the groundwork for future advances in counterfactual reasoning under noncyclic constraints.
arXiv Detail & Related papers (2026-02-08T15:51:45Z) - KBQA-R1: Reinforcing Large Language Models for Knowledge Base Question Answering [64.62317305868264]
We present textbfKBQA-R1, a framework that shifts the paradigm from text imitation to interaction optimization via Reinforcement Learning.<n>Treating KBQA as a multi-turn decision process, our model learns to navigate the knowledge base using a list of actions.<n>Experiments on WebQSP, GrailQA, and GraphQuestions demonstrate that KBQA-R1 achieves state-of-the-art performance.
arXiv Detail & Related papers (2025-12-10T17:45:42Z) - BRIDGE: Building Representations In Domain Guided Program Verification [67.36686119518441]
BRIDGE decomposes verification into three interconnected domains: Code, Specifications, and Proofs.<n>We show that this approach substantially improves both accuracy and efficiency beyond standard error feedback methods.
arXiv Detail & Related papers (2025-11-26T06:39:19Z) - SSR: Socratic Self-Refine for Large Language Model Reasoning [78.62319252287938]
Socratic Self-Refine (SSR) is a novel framework for fine-grained evaluation and precise refinement of Large Language Models (LLMs)<n>Our proposed SSR decomposes model responses into verifiable (sub-question, sub-answer) pairs, enabling step-level confidence estimation.<n> Empirical results across five reasoning benchmarks and three LLMs show that SSR consistently outperforms state-of-the-art iterative self-refinement baselines.
arXiv Detail & Related papers (2025-11-13T18:47:07Z) - Efficient and Reliable Hitting-Set Computations for the Implicit Hitting Set Approach [11.039737232955039]
The implicit hitting set (IHS) approach offers a general framework for solving computationally hard optimization problems.<n>We explore alternative algorithmic techniques for hitting set optimization based on different ways of employing pseudo-Boolean (PB) reasoning.<n>We show that exact HS computations instantiated via PB reasoning can be made competitive with a numerically exact IP solver.
arXiv Detail & Related papers (2025-08-09T15:27:36Z) - Computational Reasoning of Large Language Models [51.629694188014064]
We introduce textbfTuring Machine Bench, a benchmark to assess the ability of Large Language Models (LLMs) to execute reasoning processes.<n> TMBench incorporates four key features: self-contained and knowledge-agnostic reasoning, a minimalistic multi-step structure, controllable difficulty, and a theoretical foundation based on Turing machine.
arXiv Detail & Related papers (2025-04-29T13:52:47Z) - BRiTE: Bootstrapping Reinforced Thinking Process to Enhance Language Model Reasoning [78.63421517563056]
Large Language Models (LLMs) have demonstrated remarkable capabilities in complex reasoning tasks.<n>We present a unified probabilistic framework that formalizes LLM reasoning through a novel graphical model.<n>We introduce the Bootstrapping Reinforced Thinking Process (BRiTE) algorithm, which works in two steps.
arXiv Detail & Related papers (2025-01-31T02:39:07Z) - StructTest: Benchmarking LLMs' Reasoning through Compositional Structured Outputs [78.84060166851805]
StructTest is a novel benchmark that evaluates large language models (LLMs) on their ability to follow compositional instructions and generate structured outputs.<n> Assessments are conducted deterministically using a rule-based evaluator, which can be easily extended to new tasks and datasets.<n>We demonstrate that StructTest remains challenging even for top-performing models like Deepseek-V3/R1 and GPT-4o.
arXiv Detail & Related papers (2024-12-23T22:08:40Z) - BPP-Search: Enhancing Tree of Thought Reasoning for Mathematical Modeling Problem Solving [11.596474985695679]
We release the StructuredOR dataset, annotated with comprehensive labels that capture the complete mathematical modeling process.<n>We propose BPP-Search, an algorithm that integrates reinforcement learning into a tree-of-thought structure.
arXiv Detail & Related papers (2024-11-26T13:05:53Z) - SEER: Facilitating Structured Reasoning and Explanation via Reinforcement Learning [29.514755268807868]
We propose SEER, a novel method that maximizes a structure-based return to facilitate structured reasoning and explanation.
Our proposed structure-based return precisely describes the hierarchical and branching structure inherent in structured reasoning.
Our experiments show that SEER significantly outperforms state-of-the-art methods.
arXiv Detail & Related papers (2024-01-24T06:10:51Z) - Extended High Utility Pattern Mining: An Answer Set Programming Based
Framework and Applications [0.0]
Rule-based languages like ASP seem well suited for specifying user-provided criteria to assess pattern utility.
We introduce a new framework that allows for new classes of utility criteria not considered in the previous literature.
We exploit it as a building block for the definition of an innovative method for predicting ICU admission for COVID-19 patients.
arXiv Detail & Related papers (2023-03-23T11:42:57Z) - LRA: an accelerated rough set framework based on local redundancy of
attribute for feature selection [81.19294803707648]
We propose the LRA framework for accelerating rough set algorithms.
It is a general-purpose framework which can be applied to almost all rough set methods significantly.
arXiv Detail & Related papers (2020-10-31T08:50:28Z)
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.