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..
Compatible-Based Conditioning in Interval-Based Possibilistic Logic
Authors: Salem Benferhat, Amélie Levray, Karim Tabia, Vladik Kreinovich
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper focuses on the fundamental issue of conditioning in the interval-based possibilistic setting. The first part of the paper first proposes a set of natural properties that an interval-based conditioning operator should satisfy. We then give a natural and safe definition for conditioning an interval-based possibility distribution. This definition is based on applying standard min-based or product-based conditioning on the set of all associated compatible possibility distributions. We analyze the obtained posterior distributions and provide a precise characterization of lower and upper endpoints of the intervals associated with interpretations. The second part of the paper provides an equivalent syntactic computation of interval-based conditioning when interval-based distributions are compactly encoded by means of interval-based possibilistic knowledge bases. We show that intervalbased conditioning is achieved without extra computational cost comparing to conditioning standard possibilistic knowledge bases. |
| Researcher Affiliation | Academia | Salem Benferhat, Am elie Levray, Karim Tabia Univ Lille Nord de France, F-59000 Lille, France UArtois, CRIL CNRS UMR 8188, F-62300 Lens, France EMAIL Vladik Kreinovich Department of Computer Science University of Texas at El Paso, 500 W. University El Paso, Texas 79968, USA EMAIL |
| Pseudocode | Yes | Algorithm 1 summarizes the main steps for computing IKφ. |
| Open Source Code | No | The paper does not provide any information or links regarding open-source code for the described methodology. |
| Open Datasets | No | This paper is theoretical and does not involve experimental training on datasets. No dataset information or access is provided. |
| Dataset Splits | No | This paper is theoretical and does not involve experimental validation. No validation dataset splits are mentioned. |
| Hardware Specification | No | The paper is theoretical and does not describe specific hardware used for any experiments or computations. |
| Software Dependencies | No | The paper mentions 'SAT solver' and 'Python' in relation to computational complexity, but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | No | The paper is theoretical and does not include details about an experimental setup, hyperparameters, or training configurations. |