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..
A Tensor-Based Formalization of the Event Calculus
Authors: Efthimis Tsilionis, Alexander Artikis, Georgios Paliouras
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the scalability of our approach with the use of large datasets from a real-world application domain, and show it outperforms significantly symbolic EC, in terms of processing time. 4 Empirical Analysis We present an empirical analysis on real datasets from the field of maritime monitoring. 4.1 Experimental Setup 4.2 Experimental Results |
| Researcher Affiliation | Academia | Department of Informatics & Telecommunications, National and Kapodistrian University of Athens, Greece Institute of Informatics & Telecommunications, NCSR Demokritos , Greece Department of Maritime Studies, University of Piraeus, Greece |
| Pseudocode | No | No explicit pseudocode or algorithm blocks were found. |
| Open Source Code | Yes | The source code of both methods and a subset of one of the datasets, are available in the Code & Data Appendix. |
| Open Datasets | Yes | We employed two datasets for our empirical analysis; the first is a publicly available dataset, concerning approx. 5K vessels sailing in the Atlantic Ocean around the port of Brest, France, and consists of approx. 15M SDEs. |
| Dataset Splits | No | The paper describes a streaming setup with a sliding window approach for data processing, rather than traditional train/validation/test dataset splits. It states: 'The recognition at each qi is performed over the SDEs (input) that fall within a specified interval, the working memory or window ω. All SDEs outside the window are discarded and not considered during recognition.' |
| Hardware Specification | Yes | The experiments were performed on a single core, on a computer with AMD EPYC 7543 and 400 GB of RAM |
| Software Dependencies | Yes | running Debian GNU/Linux 12, XSB Prolog 5.0.0 and Python 3.11.4. |
| Experiment Setup | Yes | To simulate a streaming behavior, the datasets are stored in CSV files and are processed periodically in chunks according to the window ω specification. Moreover, the slide step (distance between consecutive query times) is set equal to ω in the experiments, i.e., non-overlapping windows are used. In both datasets, we employ temporal windows of four different sizes |