Approximation Algorithms for Preference Aggregation Using CP-Nets
Authors: Abu Mohammad Hammad Ali, Boting Yang, Sandra Zilles
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper studies the design and analysis of approximation algorithms for aggregating preferences over combinatorial domains, represented using Conditional Preference Networks (CP-nets). |
| Researcher Affiliation | Academia | University of Regina hammad@uregina.ca, boting.yang@uregina.ca, sandra.zilles@uregina.ca |
| Pseudocode | Yes | Algorithm 1: Build CPT(Na, Vn) that minimizes f T subject to Pa(Na, Vn) Pa(Ns, Vn) for some s {1, . . . , t} and Algorithm 2: Build CPT(Na, Vn) that minimizes f T with Pa(Na, Vn) P for given T and P |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of open-source code for the described methodology. |
| Open Datasets | No | This paper focuses on theoretical analysis and algorithm design. It does not conduct experiments on datasets, thus no information about public datasets is provided. |
| Dataset Splits | No | The paper presents theoretical results and algorithms, without performing empirical validation or using datasets with defined splits for training, validation, or testing. |
| Hardware Specification | No | The paper focuses on theoretical aspects of algorithm design and analysis, and therefore does not mention any specific hardware used for experiments. |
| Software Dependencies | No | The paper describes theoretical algorithms and their properties. It does not include details on specific software dependencies with version numbers as it does not report on practical implementations or empirical experiments. |
| Experiment Setup | No | This paper is theoretical in nature, focusing on algorithm design and analysis. As such, it does not include details about an experimental setup, hyperparameters, or training configurations. |