Intelligent scheduling
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
An Integrated Framework for Empirical Discovery
Machine Learning
Scaling up: distributed machine learning with cooperation
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
A comparison of coordinated planning methods for cooperating rovers
AGENTS '00 Proceedings of the fourth international conference on Autonomous agents
Task selection problem under uncertainty as decision-making
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 3
Casper: Space Exploration through Continuous Planning
IEEE Intelligent Systems
Deliberation Levels in Theoretic-Decision Approaches for Task Allocation in Resource-Bounded Agents
Balancing Reactivity and Social Deliberation in Multi-Agent Systems, From RoboCup to Real-World Applications (selected papers from the ECAI 2000 Workshop and additional contributions)
Decision-Theoretic Control of Planetary Rovers
Revised Papers from the International Seminar on Advances in Plan-Based Control of Robotic Agents,
LUNARES: lunar crater exploration with heterogeneous multi robot systems
Intelligent Service Robotics
The complexity of decentralized control of Markov decision processes
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Challenges in building very large teams
MMAS'04 Proceedings of the First international conference on Massively Multi-Agent Systems
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This paper describes an integrated system for coordinating multiple rover behavior with the overall goal of collecting planetary surface data. The MultiRover Integrated Science Understanding System combines concepts from machine learning with planning and scheduling to perform autonomous scientific exploration by cooperating rovers. The integrated system utilizes a novel machine learning clustering component to analyze science data and direct new science activities. A planning and scheduling system is employed to generate rover plans for achieving science goals and to coordinate activities among rovers. We describe each of these components and discuss some of the key integration issues that arose during development and influenced both system design and performance.