Temporal Specification Optimisation for the Event Calculus
Authors: Periklis Mantenoglou, Alexander Artikis
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Moreover, we present a compiler for the translation, and a reproducible empirical evaluation on real applications. Our compiler reduced the size of these knowledge bases by orders of magnitude, leading to significant reasoning efficiency gains. |
| Researcher Affiliation | Academia | 1NCSR Demokritos , Greece 2University of Piraeus, Greece EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 outlines the steps of the compiler. For each input definition (Rs, Re) of a simple FVP F=V , the compiler works as follows. |
| Open Source Code | Yes | We developed an open-source compiler1 that optimises an event description by replacing all translatable simple FVP definitions with their equivalent statically determined FVP definitions. ... Our experiments are reproducible; our compiler, the input and compiled event descriptions, and the datasets are publicly available1. 1https://github.com/periklismant/aaai2025 supplementary |
| Open Datasets | Yes | We used CAVIAR2, a benchmark activity recognition dataset. ... We used a publicly available dataset3, containing 18M AIS signals... ... We used real data, collected from the public transport vehicles in Helsinki, Finland, in November 2011 (Artikis et al. 2015). Our experiments are reproducible; our compiler, the input and compiled event descriptions, and the datasets are publicly available1. |
| Dataset Splits | No | The paper discusses evaluating RTEC by processing input streams using a sliding window and measuring reasoning times as the window size increased. This describes an operational mode and an evaluation parameter, rather than explicit training/test/validation dataset splits typically found for model development or evaluation. |
| Hardware Specification | Yes | RTEC4 operated on SWI-8.4 Prolog on a PC with Ubuntu 22, Ryzen 7 5700U and 16GB RAM. |
| Software Dependencies | Yes | RTEC4 operated on SWI-8.4 Prolog on a PC with Ubuntu 22, Ryzen 7 5700U and 16GB RAM. |
| Experiment Setup | Yes | RTEC processes input streams using a sliding window. Figure 1 (bottom) shows the reasoning times of RTEC per window, as the window size increased. Each data point is the average of 30 queries. |