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Results: 39
Number of items: 39
  • Oliehoek, F. A., Vlassis, N., & Spaan, M. T. J. (2007). Properties of the QBG-value function. (IAS-UVA-07-04). Informatics Institute. http://www.science.uva.nl/research/isla/pub/IAS-UVA-07-04.pdf
  • Porta Pleite, J. M., Vlassis, N., Spaan, M. T. J., & Poupart, P. (2006). Point-Based Value Iteration for Continuous POMDPs. Journal of Machine Learning Research, 7, 2329-2367. http://www.science.uva.nl/research/isla/pub/porta06a.pdf
  • Spaan, M. T. J., Gordon, G. J., & Vlassis, N. (2006). Decentralized planning under uncertainty for teams of communicating agents. In Proc. Int. Joint Conference on Autonomous Agents and Multiagent Systems (pp. 249-256).
  • Open Access
    Spaan, M. T. J. (2006). Approximate planning under uncertainty in partially observable environments. [Thesis, fully internal, Universiteit van Amsterdam].
  • Spaan, M. T. J., & Vlassis, N. (2005). Planning with continuous actions in partially observable environments. In Proceedings IEEE International Conference on Robotics and Automation (pp. 3469-3474).
  • Porta Pleite, J. M., Spaan, M. T. J., & Vlassis, N. (2005). Robot planning in partially observable continuous domains. In K. Verbeeck, K. Tuyls, A. Nowé, B. Manderick, & B. Kuijpers (Eds.), Proceedings 17th Belgian-Dutch Conference on Artificial Intelligence (pp. 375-376)
  • Kok, J. R., Spaan, M. T. J., & Vlassis, N. (2005). Non-communicative multi-robot coordination in dynamic environments. Robotics and Autonomous Systems, 50(2-3), 99-114. https://doi.org/10.1016/j.robot.2004.08.003
  • Oliehoek, F. A., Spaan, M. T. J., & Vlassis, N. (2005). Best-response play in partially observable card games. In Benelearn 2005: Proceedings 14th Annual Machine Learning Conference of Belgium and the Netherlands (pp. 45-50)
  • Porta Pleite, J. M., Spaan, M. T. J., & Vlassis, N. (2005). Robot planning in partially observable continuous domains. In Robotics: Science and Systems MIT Press.
  • Spaan, M. T. J., & Vlassis, N. (2005). Perseus: Randomized point-based value iteration for POMDPs. Journal of Artificial Intelligence Research, 24, 195-220.
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