Learning to Recognize Volcanoes on Venus
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
A comparison of coordinated planning methods for cooperating rovers
AGENTS '00 Proceedings of the fourth international conference on Autonomous agents
Back to the Future for Consistency-Based Trajectory Tracking
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
A reactive planner for a model-based executive
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
A model-based approach to reactive self-configuring systems
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
A Day in an Astronaut's Life: Reflections on Advanced Planning and Scheduling Technology
IEEE Intelligent Systems
The EO-1 Autonomous Science Agent
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
Lessons learned from autonomous sciencecraft experiment
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
A product-line requirements approach to safe reuse in multi-agent systems
SELMAS '05 Proceedings of the fourth international workshop on Software engineering for large-scale multi-agent systems
Probabilistic reasoning for plan robustness
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Gaia-PL: A Product Line Engineering Approach for Efficiently Designing Multiagent Systems
ACM Transactions on Software Engineering and Methodology (TOSEM)
A product-line approach to promote asset reuse in multi-agent systems
Software Engineering for Multi-Agent Systems IV
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The Autonomous Sciencecraft Experiment (ASE) will fly onboard the Air Force TechSat-21 constellation of three spacecraft scheduled for launch in 2004. ASE uses onboard continuous planning, robust task and goal-based execution, model-based mode identification and reconfiguration, and onboard machine learning and pattern recognition to radically increase science return by enabling intelligent downlink selection and autonomous retargeting. In this paper we discuss how these AI technologies are synergistically integrated in a hybrid multi-layer control architecture to enable a virtual spacecraft science agent. We also describe our working software prototype and preparations for flight.