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.
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
