Detecting complex patterns of events with significant causal and temporal dependencies across multiple data streams is extremely difficult. Training a complicated model would require a large amount of data, which is unrealistic considering that complex events often are rare. For instance, only a tiny fraction of CCTV footage shows violence, and only a minor fraction of activities recorded in computer systems are acts of Advanced Persistent Threats (APTs). Neuro-symbolic architectures can deliver excellent results, especially when features are linked together through effective probabilistic circuits compiled from human-generated logic. Moreover, uncertainty-awareness is shown to raise the trust human operators can have when using such autonomous architectures. Indeed, there is no such thing as a certain datum in the real world: everything comes with shades of uncertainty. Traditional uncertainty estimation methodologies in AI aim at quantifying it via point probabilities, which can be more misleading than other approaches such as Bayesian statistics. Starting from the role that (probabilistic) logics has in supporting human sensemaking (Toniolo et al. 2015; Cerutti and Thimm 2019), in this talk, I will illustrate how we can encompass efficient and effective uncertainty-aware learning and reasoning in probabilistic circuits (Cerutti et al. 2019; 2021) and neural networks (Sensoy et al. 2020). I will then illustrate two neuro-symbolic architectures for complex event processing (Xing et al. 2020; Roig Vilamala et al. 2020) and discuss their uncertainty-awareness future extensions and potential real-world impact, including in cyber-threat intelligence analysis (Baroni et al. 2021).
Baroni, Pietro, Federico Cerutti, Daniela Fogli, Massimiliano Giacomin, Francesco Gringoli, Giovanni Guida, and Paul Sullivan. 2021. ‘Self-Aware Effective Identification and Response to Viral Cyber Threats’. In 2021 13th International Conference on Cyber Conflict (CyCon), 353–70.
Cerutti, Federico, Lance Kaplan, Angelika Kimmig, and Murat Sensoy. 2019. ‘Probabilistic Logic Programming with Beta-Distributed Random Variables’. In Proceedings of the AAAI Conference on Artificial Intelligence.
Cerutti, Federico, Lance M. Kaplan, Angelika Kimmig, and Murat Sensoy. 2021. ‘Handling Epistemic and Aleatory Uncertainties in Probabilistic Circuits’. Accepted Subject to Minor Corrections.
Cerutti, Federico, and Matthias Thimm. 2019. ‘A General Approach to Reasoning with Probabilities’. International Journal of Approximate Reasoning 111.
Roig Vilamala, Marc, Harry Taylor, Tianwei Xing, Luis Garcia, Mani Srivastava, Lance M. Kaplan, Alun Preece, Angelika Kimming, and Federico Cerutti. 2020. ‘A Hybrid Neuro-Symbolic Approach for Complex Event Processing (Extended Abstract)’. In Proceedings of ICLP2020.
Sensoy, Murat, Lance Kaplan, Federico Cerutti, and Maryam Saleki. 2020. ‘Uncertainty-Aware Deep Classifiers Using Generative Models’. In Proceedings of the AAAI Conference on Artificial Intelligence, 5620–27.
Toniolo, A., T.J. Norman, A. Etuk, F. Cerutti, R.W. Ouyang, M. Srivastava, N. Oren, T. Dropps, J.A. Allen, and P. Sullivan. 2015. ‘Supporting Reasoning with Different Types of Evidence in Intelligence Analysis’. In Proceedings of AAMAS 2015, 781–89.
Xing, Tianwei, Luis Garcia, Marc Roig Vilamala, Federico Cerutti, Lance M Kaplan, Alun D Preece, and Mani B Srivastava. 2020. ‘Neuroplex: Learning to Detect Complex Events in Sensor Networks through Knowledge Injection’. In Proceedings of SenSys2020, edited by Jin Nakazawa and Polly Huang, 489–502. ACM.