Publications

Export 277 results:
Author [ Title(Desc)] Type Year
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z 
N
Aguilera, S., Jiménez, S., Bolaños, D., Torre, M. - S., and Colás, J., A New Mobile Text Telephony System Based on GPRS Communications, Computers Helping People with Special Needs, 9th International Conference, {ICCHP} 2004, Paris, France, July 7-9, 2004, Proceedings, 2004, pp. 1160–1166.
Dalmau, V., A new tractable class of constraint satisfaction problems, Ann. Math. Artif. Intell., vol. 44, 2005, pp. 61-85.
Dalmau, V., A New Tractable Class of Constraint Satisfaction Problems, AMAI, 2000.
Geffner, H., Non-classical Planning with a Classical Planner: The Power of Transformations, Logics in Artificial Intelligence, Springer International Publishing, 2014, pp. 33–47.
O
Neu, G., and Valko, M., Online combinatorial optimization with stochastic decision sets and adversarial losses, Advances in Neural Information Processing Systems 27 (NIPS), 2014, pp. 2780-2788.
Zimin, A., and Neu, G., Online learning in episodic Markovian decision processes by relative entropy policy search, Advances in Neural Information Processing Systems 26, 2013, pp. 1583–1591.
Kocák, T., Neu, G., and Valko, M., Online learning with Erdős-Rényi side-observation graphs, Uncertainty in Artificial Intelligence, 2016, pp. 339–347.
Kocák, T., Neu, G., and Valko, M., Online learning with noisy side observations, International Conference on Artificial Intelligence and Statistics, 2016, pp. 1186–1194.
Neu, G., György, A., and Szepesvári, \relaxCs., The Online Loop-free Stochastic Shortest-Path Problem, {Proceedings of the 23rd Annual Conference on Learning Theory (COLT)}, 2010, pp. 231–243.
Neu, G., György, A., Szepesvári, \textC., and Antos, A., Online Markov Decision Processes under Bandit Feedback, IEEE Transactions on Automatic Control, vol. 59, 2013, pp. 676–691.
Neu, G., György, A., Szepesvári, \relaxCs., and Antos, A., Online Markov Decision Processes under Bandit Feedback, Advances in Neural Information Processing Systems 23 (NIPS), 2010, pp. 1804–1812.
Thalmeier, D., Gómez, V., and Kappen, H. J., Optimal Control of Network Structure Growth, NIPS 2016 Workshop: Advances in Approximate Bayesian Inference, 2016.
Kumar, S., Jiménez, S., Py, F., Khemani, D., and Rajan, K., Optimizing Hidden Markov Models for Ocean Feature Detection, Proceedings of the Twenty-Fourth International Florida Artificial Intelligence Research Society Conference, May 18-20, 2011, Palm Beach, Florida, {USA}, 2011.
Rankothge, W., Le, F., Russo, A., and Lobo, J., Optimizing resource allocation for virtualized network functions in a cloud center using genetic algorithms, IEEE Transactions on Network and Service Management, vol. 14, 2017, pp. 343–356.
P
Jiménez, S., Fernández, F., and Borrajo, D., The PELA Architecture: Integrating Planning and Learning to Improve Execution, Proceedings of the Twenty-Third {AAAI} Conference on Artificial Intelligence, {AAAI} 2008, Chicago, Illinois, USA, July 13-17, 2008, 2008, pp. 1294–1299.
Geffner, H., Perspectives on Artificial Intelligence Planning, Proc. 18th Nat. Conf. on Artificial Intelligence (AAAI-97), AAAI/MIT Press, 2002, pp. 1013–1023.
Bailey, D. D., Dalmau, V., and Kolaitis, P. G., Phase Transitions of PP-Complete Satisfiability Problems, IJCAI, 2001, pp. 183-192.
Bailey, D. D., Dalmau, V., and Kolaitis, P. G., Phase transitions of PP-complete satisfiability problems, Discrete Applied Mathematics, vol. 155, 2007, pp. 1627-1639.
Palacios, H., and Geffner, H., Planning as Branch and Bound: A Constraint Programming Implementation, Proc. XXVIII Conf. Latinoamericana de Informática, 2002, pp. 239–251.
Bonet, B., and Geffner, H., Planning as heuristic search: New results, Recent Advances in AI Planning, Springer, 2000, pp. 360–372.
Geffner, H., Planning Graphs and Knowledge Compilation, Proceedings of the Fourth Int. Conf. on Principles of Knowledge Representation and Reasoning (KR-04), D. Dubois, Welty, C., and Williams, M., AAAI Press, 1994, pp. 662–672.
Gimenez, O., and Jonsson, A., Planning over Chain Causal Graphs for Variables with Domains of Size 5 Is NP-Hard, Journal of Artificial Intelligence Research, vol. 34, 2009, pp. 675-706.
Bonet, B., and Geffner, H., Planning under partial observability by classical replanning: Theory and experiments, Proceedings of the Twenty-Second international joint conference on Artificial Intelligence-Volume Volume Three, AAAI Press, 2011, pp. 1936–1941.
Bonet, B., and Geffner, H., Planning with Incomplete Information as Heuristic Search in Belief Space, Proc. of AIPS-00, 2000.
Segovia-Aguas, J., Ferrer-Mestres, J., and Jonsson, A., Planning with Partially Specified Behaviors, Proceedings of the 19th International Conference of the Catalan Association for Artificial Intelligence (CCAI'16), 2016.

Pages