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 [1].
Improved Algorithms for Allen's Interval Algebra: a Dynamic Programming Approach
Authors: Leif Eriksson, Victor Lagerkvist
IJCAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper we narrow this gap by presenting two novel algorithms for temporal CSPs based on dynamic programming. The ο¬rst algorithm solves temporal CSPs limited to constraints of arity three in O (3n) time, and we use this algorithm to solve A in O ((1.5922n)n) time. The second algorithm tackles A directly and solves it in O ((1.0615n)n) |
| Researcher Affiliation | Academia | Leif Eriksson and Victor Lagerkvist Department of Computer and Information Science, Link oping University, Link oping, Sweden |
| Pseudocode | Yes | Algorithm 1 DP algorithm for CSP(Ξ(3) < ). |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code of the described methodology. |
| Open Datasets | No | The paper describes theoretical algorithms and their complexity analysis, and does not involve empirical training on datasets; therefore, no information about publicly available datasets for training is provided. |
| Dataset Splits | No | The paper focuses on theoretical algorithm design and complexity analysis, and does not involve dataset validation or specific splits for empirical reproduction. |
| Hardware Specification | No | The paper describes theoretical algorithms and does not provide any specific hardware specifications used for running experiments. |
| Software Dependencies | No | The paper describes theoretical algorithms and does not specify any software dependencies with version numbers required for reproduction. |
| Experiment Setup | No | The paper focuses on theoretical algorithm design and complexity analysis, and therefore does not include details on experimental setup such as hyperparameters or training configurations. |