DeepProbCEP: A neuro-symbolic approach for complex event processing in adversarial settings. (1st April 2023)

• Record Type: Journal Article
• Authors: Roig Vilamala, Marc; Xing, Tianwei; Taylor, Harrison; Garcia, Luis; Srivastava, Mani; Kaplan, Lance; Preece, Alun; Kimmig, Angelika; Cerutti, Federico
• Is Part Of: Expert systems with applications. Volume 215(2023)
• Publication Date: 2023-04-01
• Subjects: Complex Event Processing -- Neuro-symbolic architecture -- Learning with sparse data -- Robustness; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.119376
• Languages: English
• ISSNs: 0957-4174

Abstract

Abstract: Detecting complex events from subsymbolic data streams (such as images, audio recordings or videos) is a challenging problem, as traditional symbolic approaches cannot be used to process subsymbolic data, and neural-only approaches usually require larger amounts of training data than available. In this paper, we present DeepProbCEP, a Complex Event Processing (CEP) approach designed with four objectives: (i) allowing the use of subsymbolic data as an input, (ii) retaining flexibility and modularity in the definition of complex event rules, (iii) limiting the cost of obtaining training data and (iv) being robust against adversarial conditions. DeepProbCEP archives this by using a neuro-symbolic approach, which combines the neural and symbolic approaches to allow training with sparse data. This is made possible through the injection of human knowledge. In this paper, we demonstrate that DeepProbCEP outperforms other state-of-the-art approaches when training using sparse data. We also show that DeepProbCEP is robust in different adversarial settings. Finally, DeepProbCEP's flexibility is demonstrated by showing it can be used to process both images and audio as input. Highlights: A neuro-symbolic approach for Complex Event Processing on subsymbolic data. Our approach provides flexibility when defining the rules for complex events. Our approach reduces the cost of obtaining training data. Our approach is robust against adversarial conditions.