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..
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. |