| Stochastic dynamic programming with factored representations C Boutilier, R Dearden, M Goldszmidt Artificial intelligence 121 (1-2), 49-107, 2000 | 539 | 2000 |
| Exploiting structure in policy construction C Boutilier, R Dearden, M Goldszmidt IJCAI 14, 1104-1113, 1995 | 520 | 1995 |
| Bayesian Q-learning R Dearden, N Friedman, S Russell Aaai/iaai, 761-768, 1998 | 490 | 1998 |
| Model-based Bayesian exploration R Dearden, N Friedman, D Andre arXiv preprint arXiv:1301.6690, 2013 | 332 | 2013 |
| Abstraction and approximate decision-theoretic planning R Dearden, C Boutilier Artificial Intelligence 89 (1-2), 219-283, 1997 | 222 | 1997 |
| Planning under continuous time and resource uncertainty: A challenge for AI J Bresina, R Dearden, N Meuleau, S Ramkrishnan, D Smith, ... arXiv preprint arXiv:1301.0559, 2012 | 208 | 2012 |
| High-frequency network activity, global increase in neuronal activity, and synchrony expansion precede epileptic seizures in vitro P Jiruska, J Csicsvari, AD Powell, JE Fox, WC Chang, M Vreugdenhil, X Li, ... Journal of Neuroscience 30 (16), 5690-5701, 2010 | 140 | 2010 |
| Using abstractions for decision-theoretic planning with time constraints C Boutilier, R Dearden AAAI, 1016-1022, 1994 | 138 | 1994 |
| Dynamic programming for structured continuous Markov decision problems Z Feng, R Dearden, N Meuleau, R Washington Proceedings of the 20th conference on Uncertainty in artificial intelligence …, 2004 | 131* | 2004 |
| Diagnosis by a waiter and a mars explorer N De Freitas, R Dearden, F Hutter, R Morales-Menendez, J Mutch, ... Proceedings of the IEEE 92 (3), 455-468, 2004 | 125 | 2004 |
| Particle filters for real-time fault detection in planetary rovers R Dearden, D Clancy NATIONAL AERONAUTICS AND SPACE ADMINISTRATION MOFFETT FIELD CA AMES …, 2002 | 124 | 2002 |
| Robot task planning and explanation in open and uncertain worlds M Hanheide, M Göbelbecker, GS Horn, A Pronobis, K Sjöö, A Aydemir, ... Artificial Intelligence 247, 119-150, 2017 | 120 | 2017 |
| Real-time fault detection and situational awareness for rovers: Report on the mars technology program task R Dearden, T Willeke, R Simmons, V Verma, F Hutter, S Thrun 2004 IEEE Aerospace Conference Proceedings (IEEE Cat. No. 04TH8720) 2, 826-840, 2004 | 87 | 2004 |
| Incremental contingency planning R Dearden, N Meuleau, S Ramakrishnan, DE Smith, R Washington | 82 | 2003 |
| Exploiting probabilistic knowledge under uncertain sensing for efficient robot behaviour M Hanheide, C Gretton, R Dearden, N Hawes, J Wyatt, A Pronobis, ... IJCAI, 2442-2449, 2011 | 77 | 2011 |
| The gaussian particle filter for diagnosis of non-linear systems F Hutter, R Dearden IFAC Proceedings Volumes 36 (5), 909-914, 2003 | 69 | 2003 |
| Approximating value trees in structured dynamic programming C Boutilier, R Dearden MACHINE LEARNING-INTERNATIONAL WORKSHOP THEN CONFERENCE-, 54-62, 1996 | 66 | 1996 |
| Planning to see: A hierarchical approach to planning visual actions on a robot using POMDPs M Sridharan, J Wyatt, R Dearden Artificial Intelligence 174 (11), 704-725, 2010 | 62 | 2010 |
| Continuous time particle filtering B Ng, A Pfeffer, R Dearden International joint conference on artificial intelligence 19, 1360, 2005 | 55 | 2005 |
| HiPPo: Hierarchical POMDPs for Planning Information Processing and Sensing Actions on a Robot. M Sridharan, JL Wyatt, R Dearden ICAPS, 346-354, 2008 | 49 | 2008 |
Craig BoutilierPrincipal Scientist, GoogleDirección de correo verificada de google.com
