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Results: 67
Number of items: 67
  • Open Access
    Loftin, R., Çelikok, M. M., van Hoof, H., Kaski, S., & Oliehoek, F. A. (2024). Uncoupled Learning of Differential Stackelberg Equilibria with Commitments. In AAMAS '24: Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems : May 6-10, 2024, Auckland, New Zealand (pp. 1265-1273). International Foundation for Autonomous Agents and Multiagent Systems. https://www.ifaamas.org/Proceedings/aamas2024/pdfs/p1265.pdf
  • Open Access
    Naszádi, K., Oliehoek, F. A., & Monz, C. (2024). Communicating with Speakers and Listeners of Different Pragmatic Levels. In Y. Al-Onaizan, M. Bansal, & Y.-N. Chen (Eds.), The 2024 Conference on Empirical Methods in Natural Language Processing : Proceedings of the Conference: EMNLP 2024 : November 12-16, 2024 (pp. 21777-21783). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.emnlp-main.1213
  • Open Access
    van der Pol, E. (2023). Symmetry and structure in deep reinforcement learning. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Van Der Pol, E., Worrall, D., Van Hoof, H., Oliehoek, F., & Welling, M. (2021). MDP homomorphic networks: Group symmetries in reinforcement learning. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, & H. Lin (Eds.), 34th Concerence on Neural Information Processing Systems (NeurIPS 2020): online, 6-12 December 2020 (Vol. 6, pp. 4199-4210). (Advances in Neural Information Processing Systems; Vol. 33). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper/2020/hash/2be5f9c2e3620eb73c2972d7552b6cb5-Abstract.html
  • van der Pol, E., Kipf, T., Oliehoek, F. A., & Welling, M. (2020). Plannable Approximations to MDP Homomorphisms: Equivariance under Actions. In AAMAS'20: proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems : May 9-13, 2020, Auckland, New Zealand (pp. 1431–1439). International Foundation for Autonomous Agents and Multiagent Systems. https://dl.acm.org/doi/10.5555/3398761.3398926
  • Open Access
    Akata, Z., Balliet, D., de Rijke, M., Dignum, F., Dignum, V., Eiben, G., Fokkens, A., Grossi, D., Hindriks, K., Hoos, H., Hung, H., Jonker, C., Monz, C., Neerincx, M., Oliehoek, F., Prakken, H., Schlobach, S., van der Gaag, L., van Harmelen, F., ... Welling, M. (2020). A Research Agenda for Hybrid Intelligence: Augmenting Human Intellect With Collaborative, Adaptive, Responsible, and Explainable Artificial Intelligence. Computer, 53(8), 18-28. https://doi.org/10.1109/MC.2020.2996587
  • Oliehoek, F. A., Savani, R., Gallego, J., van der Pol, E., & Groß, R. (2019). Beyond Local Nash Equilibria for Adversarial Networks. In M. Atzmueller, & W. Duivesteijn (Eds.), Artificial Intelligence: 30th Benelux Conference, BNAIC 2018, ‘s-Hertogenbosch, The Netherlands, November 8–9, 2018 : revised selected papers (pp. 73-89). (Communications in Computer and Information Science; Vol. 121). Springer. https://doi.org/10.1007/978-3-030-31978-6_7
  • Open Access
    Satsangi, Y. (2019). Active perception for person tracking. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Satsangi, Y., Whiteson, S., Oliehoek, F., & Spaan, M. T. J. (2018). Exploiting Submodular Value Functions for Scaling Up Active Perception. Autonomous Robots, 42(2), 209–233. https://doi.org/10.1007/s10514-017-9666-5
  • Cao, Z., Guo, H., Zhang, J., Oliehoek, F., & Fastenrath, U. (2017). Maximizing the Probability of Arriving on Time: A Practical Q-Learning Method. In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, Twenty-Ninth Innovative Applications of Artificial Intelligence Conference, Seventh Symposium on Educational Advances in Artificial Intelligence: 4-9 February 2017, San Francisco, California, USA (Vol. 6, pp. 4481-4487). AAAI Press. https://ojs.aaai.org/index.php/AAAI/article/view/11170
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