Publications

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Jonsson, A., Jonsson, P., and Lööw, T., Limitations of Acyclic Causal Graphs for Planning, Artificial Intelligence, vol. 210, 2014, pp. 36-55.
Jonsson, A., and Gómez, V., Hierarchical Linearly-Solvable Markov Decision Problems, Proceedings of the 26th International Conference on Automated Planning and Scheduling (ICAPS'16), 2016.
Jonsson, A., The Role of Macros in Tractable Planning Over Causal Graphs, Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI'07), 2007, pp. 1936-1941.
Jonsson, A., and Barto, A., Automated State Abstraction for Options using the U-Tree Algorithm, Advances in Neural Information Processing Systems (NIPS'00), 2001, pp. 1054-1060.
Jonsson, A., The Role of Macros in Tractable Planning, Journal of Artificial Intelligence Research, vol. 36, 2009, pp. 471-511.
Jonsson, A., and Barto, A., Active Learning of Dynamic Bayesian Networks in Markov Decision Processes, Lecture Notes in Artificial Intelligence: Abstraction, Reformulation, and Approximation (SARA'07), 2007, pp. 273-284.
Jonsson, A., Parisot, C., and De Vleeschouwer, C., A Learning Approach to Interactive Browsing of Surveillance Content, Proceedings of the 4th International Conference on Distributed Smart Cameras (ICDSC'10), 2010.
Jonsson, A., Johns, J., Mehranian, H., Arroyo, I., Woolf, B., Barto, A., Fisher, D., and Mahadevan, S., Evaluating the Feasibility of Learning Student Models from Data, Proceedings of the Workshop on Educational Data Mining at AAAI'05, 2005, pp. 1-6.
Jonsson, A., Jonsson, P., and Lööw, T., When Acyclicity Is Not Enough: Limitations of the Causal Graph, Proceedings of the 23rd International Conference on Automated Planning and Scheduling (ICAPS'13), 2013.
Jonsson, A., Efficient Pruning of Operators in Planning Domains, Lecture Notes in Artificial Intelligence: Current Topics in Artificial Intelligence (CAEPIA'07), 2007, pp. 130-139.
K
Keyder, E., and Geffner, H., Heuristics for Planning with Action Costs Revisited, Proc. of European Conf. on Artificial Intelligence (ECAI-08), 2008, pp. 588–592.
Keyder, E., and Geffner, H., Set-Additive and TSP Heuristics for Planning with Action Costs and Soft Goals, Workshop on Heuristics for Domain-Independent Planning (ICAPS'07), 2007.
Klaus, J., Miesenberger, K., Zagler, W. L., and Burger, D., Computers Helping People with Special Needs, 9th International Conference, ICCHP 2004, Paris, France, July 7-9, 2004, Proceedings, Lecture Notes in Computer Science, vol. 3118, 2004.
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. \vs, Neu, G., Valko, M., and Munos, R., Efficient learning by implicit exploration in bandit problems with side observations, Advances in Neural Information Processing Systems 27 (NIPS), 2014, pp. 613-621.
Kocák, T., Neu, G., and Valko, M., Online learning with noisy side observations, International Conference on Artificial Intelligence and Statistics, 2016, pp. 1186–1194.
Kolobov, A., Mausam, M., Weld, D. S., and Geffner, H., Heuristic search for generalized stochastic shortest path MDPs, Twenty-First International Conference on Automated Planning and Scheduling, 2011.
Kominis, F., and Geffner, H., Beliefs in Multiagent Planning: From One Agent to Many, Proc. ICAPS 2015, 2015.
Kominis, F., and Geffner, H., Multiagent Online Planning with Nested Beliefs and Dialogue, Proceedings of the 27th International Conference on Automated Planning and Scheduling (ICAPS'17), 2017.
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.

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