Stream Reasoning with Cycles


Activity recognition systems detect temporal combinations of ‘low-level’ or ‘short-term’ activities on sensor data. These systems exhibit various types of uncertainty, often leading to erroneous detection. We present an extension of an interval-based activity recognition system which operates on top of a probabilistic Event Calculus implementation. Our proposed system performs on-line recognition, as opposed to batch processing, thus supporting data streams. The empirical analysis demonstrates the efficacy of our system, comparing it to interval-based batch recognition, point-based recognition, as well as structure and weight learning models.

In Proceedings of the 19th International Conference on Principles of Knowledge Representation and Reasoning
Alexander Artikis
Alexander Artikis
Associate Professor of Artificial Intelligence