Robust Learning and Reasoning for Complex Event Forecasting (EVENFLOW)

越来越多的应用程序依赖于基于人工智能的解决方案来执行关键任务，其中许多任务具有时间性质，需要处理不断发展的信息流。在这些领域减轻威胁和利用机会的关键是能够提前预测即将发生的情况和关键的复杂事件。EVENFLOW将开发用于复杂事件预测的混合学习技术，将深度学习与基于逻辑的学习和推理结合到神经符号预测模型中。设想的方法将结合(i)神经表征学习技术，能够从感知级数据流中构建基于事件的特征；（ii）强大的符号学习和推理工具，利用这些特征来综合要预测的关键情况的高级可解释模式。EVENFLOW方法的关键是学习方法的在线性质，这使得它们适用于不断发展的数据流，并允许利用丰富的领域知识，这些知识随着时间的推移逐渐可用。为了应对神经预测器的脆弱性和时间数据流的高容量/高速度，EVENFLOW技术将依赖于新颖的机器学习形式化验证技术，以及一套用于联邦训练和增量模型构建的可扩展性算法。所学到的预测者将是可解释的和可扩展的，允许充分解释的见解，及时交付，并使主动决策成为可能。EVENFLOW将在三个具有挑战性的用例中进行评估，这些用例涉及精准医疗中的肿瘤预测、智能工厂中自主运输机器人的安全和高效行为以及关键基础设施的可靠生命周期评估。

A growing number of applications rely on AI-based solutions to carry-out mission-critical tasks, many of which are of temporal nature, dealing with ever-evolving flows of information. Crucial for mitigating threats and taking advantage of opportunities in such domains, is the ability to forecast imminent situations and critical complex events ahead of time. EVENFLOW will develop hybrid learning techniques for complex event forecasting, which combine deep learning with logic-based learning and reasoning into neuro-symbolic forecasting models. The envisioned methods will combine (i) neural representation learning techniques, capable of constructing event-based features from streams of perception-level data with (ii) powerful symbolic learning and reasoning tools, that utilize such features to synthesize high-level, interpretable patterns of critical situations to be forecast. Crucial in the EVENFLOW approach is the online nature of the learning methods, which makes them applicable to evolving data flows and allows to utilize rich domain knowledge that is becoming available progressively, over time. To deal with the brittleness of neural predictors and the high volume/velocity of temporal data flows, the EVENFLOW techniques will rely on novel, formal verification techniques for machine learning, in addition to a suite of scalability algorithms for federated training and incremental model construction. The learnt forecasters will be interpretable and scalable, allowing for fully explainable insights, delivered in a timely fashion and enabling proactive decision making. EVENFLOW will be evaluated on three challenging use cases related to oncological forecasting in precision medicine, safe and efficient behaviour of autonomous transportation robots in smart factories and reliable life cycle assessment of critical infrastructure.