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SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Technical Note: \cal Q-Learning
Machine Learning
An introduction to fuzzy control (2nd ed.)
An introduction to fuzzy control (2nd ed.)
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Machine Learning
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ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
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Autonomous robotic systems
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Robotics and Autonomous Systems
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Journal of Intelligent and Robotic Systems
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International Journal of Intelligent Systems
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IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
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Journal of Intelligent and Robotic Systems
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SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
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IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
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IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Integration of Coordination Architecture and Behavior Fuzzy Learning in Quadruped Walking Robots
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Using Fuzzy Logic to Design Separation Function in Flocking Algorithms
IEEE Transactions on Fuzzy Systems
Robotics and Autonomous Systems
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We propose an extended version of adaptive fuzzy behavior hierarchies, termed Multiple Composite Levels (MCL), that allows for the proper modulation of composite behaviors over multiple levels of a behavior hierarchy, and demonstrate its effectiveness for a hybrid learning/reactive control system. Controllers using adaptive fuzzy behavior hierarchies have previously been shown to provide effective control for robots tasked with multiple concurrent tasks. However, when more complex hierarchies are used to provide control for tasks of increasing complexity, low-level reactive behaviors may not be properly weighted, resulting in sub-optimal control. Through experimental evaluation in which composite behaviors that coordinate lower behaviors are learned using reinforcement learning, we demonstrate that MCL provides effective control in a complex multi-agent task, whereas the original implementation of adaptive fuzzy behavior hierarchies does not.