A Tensor-Based Formalization of the Event Calculus
A Tensor-Based Formalization of the Event Calculus
Efthimis Tsilionis, Alexander Artikis, Georgios Paliouras
Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence
Main Track. Pages 3584-3592.
https://doi.org/10.24963/ijcai.2024/397
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.
Keywords:
Knowledge Representation and Reasoning: KRR: Non-monotonic reasoning
Knowledge Representation and Reasoning: KRR: Logic programming
Knowledge Representation and Reasoning: KRR: Qualitative, geometric, spatial, and temporal reasoning
Machine Learning: ML: Matrix/tensor methods