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Model-based approaches bear great promise for decision making of agents interacting with the physical world.
A formal basis for the heuristic determination of minimum cost paths
P. E. Hart, N. J. Nilsson, and B. Raphael · 1968
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Curious model-building control systems
Jürgen Schmidhuber · 1991
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Information-based objective functions for active data selection
David JC MacKay · 1992
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S. Thrun · 1992
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Novel approach to nonlinear/non-gaussian bayesian state estimation
N. J. Gordon, D. J. Salmond, and A. F. M. Smith · 1993
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Peter Whaite and Frank P. Ferrie · 1994
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Robot navigation: Integrating perception, environmental constraints and task execution within a probabilistic framework
Alberto Elfes · 1996
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Autonomous exploration: Driven by uncertainty
Peter Whaite and Frank P Ferrie · 1997
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Kevin P. Murphy · 1999
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Information gain-based exploration using rao-blackwellized particle filters
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Towards autonomous exploration with information potential field in 3d environments
Chaoqun Wang, Lili Meng, Teng Li, Clarence W De Silva, and Max Q-H Meng · 2017
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Autonomous reconstruction of unknown indoor scenes guided by time-varying tensor fields
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Past, present, and future of simultaneous localization and mapping: Toward the robust-perception age
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Kai Xu, Lintao Zheng, Zihao Yan, Guohang Yan, Eugene Zhang, Matthias Niessner, Oliver Deussen, Daniel Cohen-Or, and Hui Huang · 2017
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Decomposition of uncertainty in Bayesian deep learning for efficient and risk-sensitive learning
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Generative temporal models with spatial memory for partially observed environments
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Simple random search provides a competitive approach to reinforcement learning
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