Compatible-Based Conditioning in Interval-Based Possibilistic Logic
Authors: Salem Benferhat, Amélie Levray, Karim Tabia, Vladik Kreinovich
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | 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 {benferhat, levray, tabia}@cril.fr Vladik Kreinovich Department of Computer Science University of Texas at El Paso, 500 W. University El Paso, Texas 79968, USA vladik@utep.edu |
| 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. |