Multi-Context Stream Reasoning

Description and Goals

The field of artificial intelligence and knowledge representation (KR) has originated a high variety of formalisms, notions, languages, and formats during the past decades. Each approach has been motivated and designed with specific applications in mind. Nowadays, in the century of Industry 4.0, the Internet of Things, and Smart-devices, we are interested in ways to connect the various approaches and allow them to distribute and exchange their knowledge and beliefs in an uniform way. This leads to the problem that these sophisticated knowledge representation approaches cannot understand each others point of views and their positions on semantics are not necessarily compatible either. These two problems between different KR-formalisms has been tackled by the concept of Multi-Context Systems, which allow methods to transfer information under a strong and generalised notion of semantics.

Recent advances in the representation of Streams allowed one to utilise the ideas of Multi-Context Systems (MSC) and expand them to provide reasoning based on streams. Modern languages, such as LARS, extend logic programming formalisms like ASP with sliding windows and temporal modalities that allow one to encode monitoring, configuration, control, and many other problems occurring in the domains listed above. Available distributed high-performance reasoning systems are closely related to the ideas of MSCs and can be used to efficiently process data streams with low latency.

The goal of this tutorial is to provide a sophisticated and formally sound overview on the last decade of advances in the field of Multi-Context Stream Reasoning. The aim is to educate participants on how MSCs evolved into the current state-of-the-art and how they differ in terms of applicability and feasibility. In addition it should allow a conscious insight on the motivation and philosophy behind the reason for the development of different MSCs.

Speakers