Induction as nonmonotonic inference
Proceedings of the first international conference on Principles of knowledge representation and reasoning
Interactive theory revision: an inductive logic programming approach
Interactive theory revision: an inductive logic programming approach
A polynomial time computable metric between point sets
Acta Informatica
A Framework for Defining Distances Between First-Order Logic Objects
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
In search of the Horowitz factor
AI Magazine
Tractable induction and classification in first order logic via stochastic matching
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Playing Mozart phrase by phrase
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
Music compositional intelligence with an affective flavor
Proceedings of the 12th international conference on Intelligent user interfaces
Relational Sequence Alignments and Logos
Inductive Logic Programming
Learning to play like the great pianists
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
An emotion-driven musical piece generator for a constructive adaptive user interface
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
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It is well known that many hard tasks considered in machine learning and data mining can be solved in a rather simple and robust way with an instance- and distance-based approach. In this work we present another difficult task: learning, from large numbers of complex performances by concert pianists, to play music expressively. We model the problem as a multi-level decomposition and prediction task. We show that this is a fundamentally relational learning problem and propose a new similarity measure for structured objects, which is built into a relational instance-based learning algorithm named DISTALL. Experiments with data derived from a substantial number of Mozart piano sonata recordings by a skilled concert pianist demonstrate that the approach is viable. We show that the instance-based learner operating on structured, relational data outperforms a propositional k-NN algorithm. In qualitative terms, some of the piano performances produced by DISTALL after learning from the human artist are of substantial musical quality; one even won a prize in an international `computer music performance' contest. The experiments thus provide evidence of the capabilities of ILP in a highly complex domain such as music.