Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Decomposing Constraint Networks for Calculating c-Representations
Authors: Marco Wilhelm, Gabriele Kern-Isberner
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we focus on qualitative default reasoning based on Spohn s ranking functions for which network-based methods have not yet been studied satisfactorily. With constraint networks, we develop a framework for iterative calculations of c-representations... As an application of our framework, we show that skeptical c-inferences can be drawn locally from safe sub-bases without losing validity. |
| Researcher Affiliation | Academia | Marco Wilhelm, Gabriele Kern-Isberner Dept. of Computer Science, TU Dortmund University, Dortmund, Germany |
| Pseudocode | Yes | Algorithm 1: Calculation of c-representations on the basis of constraint networks |
| Open Source Code | No | The paper does not mention providing open-source code for the described methodology or a link to a code repository. |
| Open Datasets | No | The paper uses synthetic examples (e.g., 'ex = {δi | i [5]} with Σex = {a, b, c}') for illustration, but it does not utilize a publicly available dataset that would require access information. |
| Dataset Splits | No | The paper focuses on theoretical development and mathematical proofs; it does not involve empirical experiments with training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any empirical experiments, therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe any empirical experiments, therefore, no specific software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper is theoretical and focuses on algorithm design and mathematical proofs. It does not describe any empirical experimental setup details like hyperparameter values or training configurations. |