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..
Probabilistic Knowledge-Based Programs
Authors: Jérôme Lang, Bruno Zanuttini
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study succinctness and the complexity of verification for PKBPs. This paper introduces a new theoretical framework, Probabilistic Knowledge-Based Programs, and analyzes their theoretical properties such as succinctness and complexity (P/poly, NP, PP, PPP, PSPACE) in sections 5, 6, and 7. It does not report any empirical studies, dataset evaluations, or performance metrics from experiments. |
| Researcher Affiliation | Academia | J erˆome Lang CNRS-LAMSADE Universit e Paris-Dauphine, France EMAIL Bruno Zanuttini GREYC UNICAEN, CNRS, ENSICAEN, France EMAIL |
| Pseudocode | No | The paper describes the structure and semantics of PKBPs but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code, nor does it include links to a code repository. |
| Open Datasets | No | The paper focuses on theoretical aspects and does not involve experimental evaluation using datasets. |
| Dataset Splits | No | The paper is theoretical and does not describe experimental setups with training, validation, or test data splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments that would require specific hardware specifications. |
| Software Dependencies | No | The paper focuses on theoretical contributions and does not mention specific software dependencies with version numbers required for implementation or reproduction. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details such as hyperparameters or training configurations. |