Stream-Based Reasoning in DyKnow

The information available to modern autonomous systems is often in the form of streams. As the number of sensors and other stream sources increases there is a growing need for incremental reasoning about the incomplete content of sets of streams in order to draw relevant conclusions and react to new situations as quickly as possible. To act rationally, autonomous agents often depend on high level reasoning components that require crisp, symbolic knowledge about the environment. Extensive processing at many levels of abstraction is required to generate such knowledge from noisy, incomplete and quantitative sensor data. We define knowledge processing middleware as a systematic approach to integrating and organizing such processing, and argue that connecting processing components with streams provides essential support for steady and timely flows of information. DyKnow is a concrete and implemented instantiation of such middleware, providing support for stream reasoning at several levels. First, the formal KPL language allows the specification of streams connecting knowledge processes and the required properties of such streams. Second, chronicle recognition incrementally detects complex events from streams of more primitive events. Third, complex metric temporal formulas can be incrementally evaluated over streams of states. DyKnow and the stream reasoning techniques are described and motivated in the context of a UAV traffic monitoring application.