Robust Learning and Reasoning for Complex Event Forecasting (EVENFLOW)

复杂事件预测的鲁棒学习和推理 (EVENFLOW)

• 批准号: 10042508
• 金额: $ 61.95万
• 依托单位: IMPERIAL COLLEGE LONDON
• 依托单位国家: 英国
• 项目类别: EU-Funded
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=10042508
• 关键词: Robust; Learning; Reasoning; Complex; Event

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.

越来越多的应用程序依靠基于人工智能的解决方案来执行关键任务，其中许多任务具有临时性，处理不断变化的信息流。对于减轻威胁和利用这些领域的机会来说，至关重要的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）神经表示学习技术，能够从感知级数据流构建基于事件的特征；（ii）强大的符号学习和推理工具，利用这些特征来合成高级、可解释的模式需要预测的紧急情况。 EVENFLOW 方法的关键在于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，并允许利用随着时间的推移逐渐变得可用的丰富领域知识。为了解决神经预测器的脆弱性和时间数据流的高容量/高速度，EVENFLOW 技术将依赖于机器学习的新颖的形式验证技术，以及一套用于联合训练和增量模型构建的可扩展性算法。学习到的预测器将具有可解释性和可扩展性，能够及时提供完全可解释的见解并实现主动决策。 EVENFLOW 将根据三个具有挑战性的用例进行评估，这些用例涉及精准医学中的肿瘤学预测、智能工厂中自主运输机器人的安全高效行为以及关键基础设施的可靠生命周期评估。