Bridging the Sense-Reasoning Gap Using DyKnow: A Knowledge Processing Middleware Framework
KI '07 Proceedings of the 30th annual German conference on Advances in Artificial Intelligence
Knowledge Processing Middleware
SIMPAR '08 Proceedings of the 1st International Conference on Simulation, Modeling, and Programming for Autonomous Robots
A temporal logic-based planning and execution monitoring framework for unmanned aircraft systems
Autonomous Agents and Multi-Agent Systems
A UAV search and rescue scenario with human body detection and geolocalization
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
Stream-Based Reasoning Support for Autonomous Systems
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
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Any autonomous system embedded in a dynamic and changing environment must be able to create qualitative knowledge and object structures representing aspects of its environment on the fly from raw or preprocessed sensor data in order to reason qualitatively about the environment. These structures must be managed and made accessible to deliberative and reactive functionalities which are dependent on being situationally aware of the changes in both the robotic agent's embedding and internal environment. DyKnow is a software framework which provides a set of functionalities for contextually accessing, storing, creating and processing such structures. The system is implemented and has been deployed in a deliberative/reactive architecture for an autonomous unmanned aerial vehicle. The architecture itself is distributed and uses real-time CORBA as a communications infrastructure. We describe the system and show how it can be used in execution monitoring and chronicle recognition scenarios for UAV applications.