Studies in hybrid systems: modeling, analysis, and control
Studies in hybrid systems: modeling, analysis, and control
A perspective view and survey of meta-learning
Artificial Intelligence Review
Decision-Theoretic, High-Level Agent Programming in the Situation Calculus
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
LICS '96 Proceedings of the 11th Annual IEEE Symposium on Logic in Computer Science
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
IBAL: a probabilistic rational programming language
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Effective input variable selection for function approximation
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
A testbed for adaptive human-robot collaboration
KI'10 Proceedings of the 33rd annual German conference on Advances in artificial intelligence
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One central property of cognitive systems is the ability to learn and to improve continually. We present a robot control language that combines programming and learning in order to make learning executable in the normal robot program. The language constructs of our learning language RoLL rely on the concept of hierarchical hybrid automata to enable a declarative, explicit specification of learning problems. Using the example of an autonomous household robot, we point out some instances where learning-and especially continued learning-makes the robot control program more cognitive.