Abstract
To maximize its success, an AGI typically needs to explore its initially unknown world. Is there an optimal way of doing so? Here we derive an affirmative answer for a broad class of environments.
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References
Chaloner, K., Verdinelli, I.: Bayesian experimental design: A review. Statistical Science 10, 273–304 (1995)
Fedorov, V.V.: Theory of optimal experiments. Academic Press, London (1972)
Itti, L., Baldi, P.F.: Bayesian surprise attracts human attention. In: NIPS 2005, pp. 547–554 (2006)
Lindley, D.V.: On a measure of the information provided by an experiment. Annals of Mathematical Statistics 27(4), 986–1005 (1956)
Penny, W.: Kullback-liebler divergences of normal, gamma, dirichlet and wishart densities. Tech. rep., Wellcome Department of Cognitive Neurology, University College London (2001)
Schmidhuber, J.: Curious model-building control systems. In: IJCNN 1991, vol. 2, pp. 1458–1463 (1991)
Schmidhuber, J.: Formal theory of creativity, fun, and intrinsic motivation (1990-2010). Autonomous Mental Development, IEEE Trans. on Autonomous Mental Development 2(3), 230–247 (2010)
Settles, B.: Active learning literature survey. Tech. rep., University of Wisconsin Madison (2010)
Singh, S., Barto, A., Chentanez, N.: Intrinsically motivated reinforcement learning. In: NIPS 2004 (2004)
Storck, J., Hochreiter, S., Schmidhuber, J.: Reinforcement driven information acquisition in non-deterministic environments. In: ICANN 1995 (1995)
Sun, Y., Gomez, F.J., Schmidhuber, J.: Planning to be surprised: Optimal bayesian exploration in dynamic environments (2011), http://arxiv.org/abs/1103.5708
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Sun, Y., Gomez, F., Schmidhuber, J. (2011). Planning to Be Surprised: Optimal Bayesian Exploration in Dynamic Environments. In: Schmidhuber, J., Thórisson, K.R., Looks, M. (eds) Artificial General Intelligence. AGI 2011. Lecture Notes in Computer Science(), vol 6830. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22887-2_5
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DOI: https://doi.org/10.1007/978-3-642-22887-2_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22886-5
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