Recognizing User Preferences Based on Layered Activity Recognition and First-Order Logic

Only few cognitive architectures have been proposed that cover the complete range from recognizers working on the direct sensor input, to logical inference mechanisms of classical artificial intelligence (AI). Logical systems operate on abstract predicates, which are often related to an action-like state transition, especially when compared to the classes recognized by pattern recognition approaches. On the other hand, pattern recognition is often limited to static patterns, and temporal and multi-modal aspects of a class are often not regarded, e.g. by testing only on pre-segmented data. Recent trends in AI aim at developing applications and methods that are motivated by data-driven real world scenarios, while the field of pattern recognition attempts to push forward the boundary of pattern complexity. We propose a new generic architecture to close the gap between AI and pattern recognition approaches. In order to detect abstract complex patterns, we process sequential data in layers. On each layer, a set of elementary classes is recognized and the outcome of the classification is passed to the successive layer such that the time granularity increases. Layers can combine modalities, additional symbolic information or make use of reasoning algorithms. We evaluated our approach in an on-line scenario of activity recognition using three layers. The obtained results show that the combination of concepts from pattern recognition and high-level symbolic information leads to a prosperous and powerful symbiosis.