Stream reasoning is the incremental reasoning over streams of incrementally available information. This tutorial gives an overview of the state-of-the-art in efficient and scalable logic-based approaches to spatio-temporal stream reasoning with incomplete information suitable for execution monitoring of autonomous systems.The Tutorial Slides
We live in a streaming world. New information is continually being produced by sensors and humans. A stream is such a sequence of incrementally available information. Streaming information is always dynamic and temporal, and usually also spatial in nature. Reasoning over these streams is necessary to draw conclusions and make decisions in real-time. Since streams are conceptually infinite, this reasoning has to be done incrementally as new information becomes available. The incremental reasoning over streams is called stream reasoning. Stream reasoning addresses at least three of four V's in BigData: Velocity, Variety and Veracity.
This tutorial gives an overview of the state-of-the-art in efficient and scalable logic-based approaches to spatio-temporal stream reasoning with incomplete information suitable for execution monitoring of autonomous systems. Stream reasoning is an emerging research area with great potential and strategic relevance for autonomous systems, the Internet of Things and real-time data analytics. An important application is online verification of autonomous systems. These systems are too large and too complex to be modeled in detail. Therefore model checking is not a viable option. Instead, stream reasoning can be used to provide run-time verification by continually monitoring the system execution with formal guarantees to make sure that it behaves as expected. We will also show examples of how stream reasoning can be used for planning, situation awareness and other high level functionalities in autonomous systems.
The target audience are both people interested in KR, both spatio-temporal reasoning and incremental reasoning, and in autonomous systems such as robotics. Basic understanding of logics should be enough to appreciate the tutorial. Previous knowledge of autonomous systems is beneficial, but not necessary.
Stream reasoning is an emerging research area with great potential and strategic relevance for all of AI as most AI-systems running in the real world have to deal with streams of information in one form or another. Stream reasoning addresses at least three of four V's in BigData: Velocity, Variety and Veracity. Stream reasoning has recently received significant attention with new groups entering the area and an international research community being formed including people from AI, KR, Semantic Web and the Model Checking communities http://www.vcla.at/sr2015/.
The tutorial would both motivate and explain a topic of emerging importance for AI and Introduce expert non-specialists to an AI subarea.
Fredrik Heintz is an associated professor of Computer Science at Linköping University, Sweden. He has been doing research on stream reasoning and its integration in autonomous systems for more than 10 years. You could argue that the area has independently been developed by him [8, 6, 5] and the Semantic Web community [9, 3, 1, 2]. He currently leads the Stream Reasoning research group within the Artificial Intelligence and Integrated Computer Systems division. With his students they have recently extended their original metric-temporal logic stream reasoning approach for complete information to spatio-temporal stream reasoning with incomplete information [7, 4].