A Tensor-Based Formalization of the Event Calculus
Authors: Efthimis Tsilionis, Alexander Artikis, Georgios Paliouras
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | 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 |