A Tensor-Based Formalization of the Event Calculus

Efthimis Tsilionis; Alexander Artikis; Georgios Paliouras

IJCAI 2024poster1 citations

A Tensor-Based Formalization of the Event Calculus

Efthimis Tsilionis, Alexander Artikis, Georgios Paliouras

Abstract

We present a formalization of the Event Calculus (EC) in tensor spaces. The motivation for a tensor-based predicate calculus comes from the area of composite event recognition (CER). As a CER engine, we adopt a logic programming implementation of EC with optimizations for continuous narrative assimilation on data streams. We show how to evaluate EC rules algebraically and solve a linear equation to compute the corresponding models. 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.

Knowledge Representation and Reasoning: KRR: Non-monotonic reasoningKnowledge Representation and Reasoning: KRR: Logic programmingKnowledge Representation and Reasoning: KRR: Qualitative, geometric, spatial, and temporal reasoningMachine Learning: ML: Matrix/tensor methods

BibTeX

@inproceedings{ijcai2024p397,
  title     = {A Tensor-Based Formalization of the Event Calculus},
  author    = {Tsilionis, Efthimis and Artikis, Alexander and Paliouras, Georgios},
  booktitle = {Proceedings of the Thirty-Third International Joint Conference on
               Artificial Intelligence, {IJCAI-24}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Kate Larson},
  pages     = {3584--3592},
  year      = {2024},
  month     = {8},
  note      = {Main Track},
  doi       = {10.24963/ijcai.2024/397},
  url       = {https://doi.org/10.24963/ijcai.2024/397},
}