Hierarchical Representations of Behavior for Efficient Creative Search
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[1] D. Campbell. Blind variation and selective retention in creative thought as in other knowledge processes. , 1960, Psychological review.
[2] John H. Holland,et al. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .
[3] C. Watkins. Learning from delayed rewards , 1989 .
[4] Keiji Kanazawa,et al. A model for reasoning about persistence and causation , 1989 .
[5] Richard S. Sutton,et al. Integrated Modeling and Control Based on Reinforcement Learning and Dynamic Programming , 1990, NIPS 1990.
[6] John R. Koza,et al. Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.
[7] Craig Boutilier,et al. Exploiting Structure in Policy Construction , 1995, IJCAI.
[8] Doina Precup,et al. Between MDPs and Semi-MDPs: A Framework for Temporal Abstraction in Reinforcement Learning , 1999, Artif. Intell..
[9] D. Simonton. Origins of genius : Darwinian perspectives on creativity , 1999 .
[10] Craig Boutilier,et al. Stochastic dynamic programming with factored representations , 2000, Artif. Intell..
[11] L. Gabora. Creative Thought as a nonDarwinian Evolutionary Process , 2004, nlin/0411057.
[12] Sean R Eddy,et al. What is dynamic programming? , 2004, Nature Biotechnology.
[13] L. Gabora. Creative Thought as a non Darwinian Evolutionary Process , 2005 .
[14] Richard S. Sutton,et al. Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.
[15] Olivier Sigaud,et al. Learning the structure of Factored Markov Decision Processes in reinforcement learning problems , 2006, ICML.
[16] Andrew G. Barto,et al. Causal Graph Based Decomposition of Factored MDPs , 2006, J. Mach. Learn. Res..