Probabilistic Knowledge-Based Programs
Authors: Jérôme Lang, Bruno Zanuttini
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | 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 lang@lamsade.dauphine.fr Bruno Zanuttini GREYC UNICAEN, CNRS, ENSICAEN, France bruno.zanuttini@unicaen.fr |
| 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. |