Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Creative Mind: Myths and Mechanisms
Creative Mind: Myths and Mechanisms
Anticipatory Behavior in Adaptive Learning Systems
A dynamic Bayesian approach to computational Laban shape quality analysis
Advances in Human-Computer Interaction
Open problems in evolutionary music and art
EC'05 Proceedings of the 3rd European conference on Applications of Evolutionary Computing
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Automated creativity, giving a machine the ability to originate meaningful new concepts and ideas, is a significant challenge. Machine learning models make advances in this direction but are typically limited to reproducing already known material. Self-motivated reinforcement learning models present new possibilities in computational creativity, conceptually mimicking human learning to enable automated discovery of interesting or surprising patterns. This work describes a musical intrinsically motivated reinforcement learning model, built on adaptive resonance theory algorithms, towards the goal of producing humanly valuable creative music. The capabilities of the prototype system are examined through a series of short, promising compositions, revealing an extreme sensitivity to feature selection and parameter settings, and the need for further development of hierarchical models.