CNLS '89 Proceedings of the ninth annual international conference of the Center for Nonlinear Studies on Self-organizing, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks on Emergent computation
Specifying real-time properties with metric temporal logic
Real-Time Systems
The many faces of publish/subscribe
ACM Computing Surveys (CSUR)
Development environments for autonomous mobile robots: A survey
Autonomous Robots
Cayuga: a high-performance event processing engine
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
DyKnow: An approach to middleware for knowledge processing
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - AILS '04
XStream: a Signal-Oriented Data Stream Management System
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
A temporal logic-based planning and execution monitoring framework for unmanned aircraft systems
Autonomous Agents and Multi-Agent Systems
Bridging the sense-reasoning gap: DyKnow - Stream-based middleware for knowledge processing
Advanced Engineering Informatics
A stream-based hierarchical anchoring framework
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
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For autonomous systems such as unmanned aerial vehicles to successfully perform complex missions, a great deal of embedded reasoning is required at varying levels of abstraction. To support the integration and use of diverse reasoning modules we have developed DyKnow, a stream-based knowledge processing middleware framework. By using streams, DyKnow captures the incremental nature of sensor data and supports the continuous reasoning necessary to react to rapid changes in the environment. DyKnow has a formal basis and pragmatically deals with many of the architectural issues which arise in autonomous systems. This includes a systematic stream-based method for handling the sense-reasoning gap, caused by the wide difference in abstraction levels between the noisy data generally available from sensors and the symbolic, semantically meaningful information required by many high-level reasoning modules. As concrete examples, stream-based support for anchoring and planning are presented.