Adaptive planning in human search
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Maarten Speekenbrink | Arthur Guez | Eric Schulz | Moritz Krusche | A. Guez | M. Speekenbrink | Moritz Krusche | Eric Schulz
[1] D. Hassabis,et al. Neural Mechanisms of Hierarchical Planning in a Virtual Subway Network , 2016, Neuron.
[2] A. D. D. Groot. Thought and Choice in Chess , 1978 .
[3] M. Speekenbrink,et al. Putting bandits into context: How function learning supports decision making , 2016, bioRxiv.
[4] P. Dayan,et al. Adaptive integration of habits into depth-limited planning defines a habitual-goal–directed spectrum , 2016, Proceedings of the National Academy of Sciences.
[5] Jonathan D. Nelson,et al. Exploration and generalization in vast spaces 1 , 2017 .
[6] Karl J. Friston,et al. Bayesian model selection for group studies , 2009, NeuroImage.
[7] P. Dayan,et al. Model-based influences on humans’ choices and striatal prediction errors , 2011, Neuron.
[8] Andreas Krause,et al. Generalization and search in risky environments , 2017, bioRxiv.
[9] Peter Dayan,et al. Bonsai Trees in Your Head: How the Pavlovian System Sculpts Goal-Directed Choices by Pruning Decision Trees , 2012, PLoS Comput. Biol..
[10] Wei Ji Ma,et al. A computational model for decision tree search , 2017, CogSci.
[11] Peter Dayan,et al. Scalable and Efficient Bayes-Adaptive Reinforcement Learning Based on Monte-Carlo Tree Search , 2013, J. Artif. Intell. Res..
[12] Simon M. Lucas,et al. A Survey of Monte Carlo Tree Search Methods , 2012, IEEE Transactions on Computational Intelligence and AI in Games.
[13] Carl E. Rasmussen,et al. Occam's Razor , 2000, NIPS.
[14] P. Dayan,et al. Goals and Habits in the Brain , 2013, Neuron.
[15] Peter Dayan,et al. Interplay of approximate planning strategies , 2015, Proceedings of the National Academy of Sciences.
[16] Demis Hassabis,et al. Mastering the game of Go with deep neural networks and tree search , 2016, Nature.