Publications

Export 261 results:
Author Title [ Type(Desc)] Year
Book Chapter
Francès, G., Rubio-Campillo, X., Lancelotti, C., and Madella, M., Decision Making in Agent-Based Models, Multi-Agent Systems, 2014, pp. 370–378.
Albore, A., Palacios, H., and Geffner, H., Fast and Informed Action Selection for Planning with Sensing, Current Topics in Artificial Intelligence, 12th Conference of the Spanish Association for Artificial Intelligence (CAEPIA). Selected Papers, D. Borrajo, Castillo, L., and Corchado, J.Manuel, Salamanca, Spain: Springer, 2007. Paper CAEPIA 2007 (144.71 KB)
Ognibene, D., Wu, Y., Lee, K., and Demiris, Y., Hierarchies for embodied action perception, Computational and Robotic Models of the Hierarchical Organization of Behavior, Springer, 2013, p. Chapter–5.
Jiménez, S., and Turbides, Tde la Rosa, Learning-Based Planning, Encyclopedia of Artificial Intelligence {(3} Volumes), 2009, pp. 1024–1028.
Geffner, H., Non-classical Planning with a Classical Planner: The Power of Transformations, Logics in Artificial Intelligence, Springer International Publishing, 2014, pp. 33–47.
Bonet, B., and Geffner, H., Planning as heuristic search: New results, Recent Advances in AI Planning, Springer, 2000, pp. 360–372.
Chinellato, E., Ognibene, D., Sartori, L., and Demiris, Y., Time to Change: Deciding When to Switch Action Plans during a Social Interaction, Biomimetic and Biohybrid Systems, Springer Berlin Heidelberg, 2013, pp. 47–58.
Conference Paper
Albore, A., and Geffner, H., Acting in Partially Observable Environments When Achievement of the Goal Cannot be Guaranteed, Workshop on Planning and Plan Execution for Real-World Systems (ICAPS'09), Thessaloniki, Greece: 2009. Paper (165.67 KB)
Bonet, B., and Geffner, H., Action Selection for MDPs: Anytime AO* Versus UCT., AAAI, 2012.
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.
Trigo, P., Jonsson, A., and Coelho, H., Active Learning of Dynamic Bayesian Networks in Markov Decision Processes, Lecture Notes in Artificial Intelligence: Advances in Artificial Intelligence (IBERAMIA'06), 2006, pp. 37-47.
Geffner, H., and Haslum, P., Admissible heuristics for optimal planning, Proceedings of the 5th Internat. Conf. of AI Planning Systems (AIPS 2000), 2000.
Neu, G., György, A., and Szepesvári, \textC., The adversarial stochastic shortest path problem with unknown transition probabilities, Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, N. Lawrence and Girolami, M., 2012, pp. 805–813.
Bonet, B., and Geffner, H., An algorithm better than AO*?, Proc. AAAI-05, 2005.
Ognibene, D., and Baldassarre, G., APPRENDIMENTO PER RINFORZO E CODIFICA TRAMITE POPOLAZIONE NEURALE: UN MODELLO PER IL REACHING APPLICATO A DUE TASK, WIVA3 - 3rd Workshop Italiano Vita Artificial, Siena, Italy: 2006.
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.
Lotinac, D., Segovia-Aguas, J., Jiménez, S., and Jonsson, A., Automatic Generation of High-Level State Features for Generalized Planning, Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI'16), 2016.
Lotinac, D., and Jonsson, A., Automatic Generation of HTNs from PDDL, Proceedings of the Planning and Learning (PAL) Workshop at the International Conference on Automated Planning and Scheduling (ICAPS'15), 2015, pp. 15-23.
Lotinac, D., and Jonsson, A., Automatic Generation of HTNs from PDDL, Proceedings of the 2nd Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM'15), 2015.
Nivel, E., Thórisson, K. R., Steunebrink, B. R., Dindo, H., Pezzulo, G., Rodrıguez, M., Hernández, C., Ognibene, D., Schmidhuber, J., Sanz, R., and , AUTONOMOUS ACQUISITION OF NATURAL LANGUAGE, IADIS International Conference on Intelligent Systems & Agents, 2014, pp. 58–66.
Kominis, F., and Geffner, H., Beliefs in Multiagent Planning: From One Agent to Many, Proc. ICAPS 2015, 2015.
Lipovetzky, N., and Geffner, H., Best-first Width Search: Exploration and Exploitation in Classical Planning, Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI'17), 2017.
Chen, H., and Dalmau, V., Beyond Hypertree Width: Decomposition Methods Without Decompositions, CP, 2005, pp. 167-181.
Dalmau, V., Boolean Formulas are Hard to Learn for most Gate Bases, ALT, 1999, pp. 301-312.

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