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..
On the Complexity of mCP-nets
Authors: Thomas Lukasiewicz, Enrico Malizia
AAAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we start to fill this gap by carrying out a precise computational complexity analysis of voting tasks on acyclic binary polynomially connected m CP-nets whose constituents are standard CP-nets. |
| Researcher Affiliation | Academia | Thomas Lukasiewicz and Enrico Malizia Department of Computer Science, University of Oxford, UK firstnameEMAIL |
| Pseudocode | No | The paper describes conceptual algorithms and complexity analysis but does not provide any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper is theoretical and does not mention releasing any source code for its methodology. |
| Open Datasets | No | This is a theoretical paper focusing on computational complexity analysis; it does not utilize or provide access to datasets for training or evaluation. |
| Dataset Splits | No | This is a theoretical paper; it does not describe experimental validation with dataset splits. |
| Hardware Specification | No | This is a theoretical paper on computational complexity; no specific hardware used for experiments is mentioned. |
| Software Dependencies | No | This is a theoretical paper; no specific software dependencies with version numbers are mentioned. |
| Experiment Setup | No | This is a theoretical paper on computational complexity; it does not describe any experimental setup details or hyperparameters. |