The DOI matches its Crossref record; 55 of the 55 references it cites were found in a registry or an index.
Partially observable Markov decision processes for spoken dialog systems
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Citations
Cites
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225
Reinforcement Learning: An Introduction
(2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 24,496
Watkins, C. (1989) · OpenGrey (Institut de l'Information Scientifique et Technique) · doi:10.17863/cam.132164 · Cited by 5,297
Planning and acting in partially observable stochastic domains
Kaelbling, L. P., Littman, M. L., & Cassandra, A. R. (1998) · Artificial Intelligence · doi:10.1016/s0004-3702(98)00023-x · Cited by 4,149
Bayesian Networks and Decision Graphs
Jensen, F. V., & Nielsen, T. D. (2007) · Information Science and Statistics · doi:10.1007/978-0-387-68282-2 · Cited by 4,129
Speech and language processing
(2010) · Day-to-Day Dyslexia in the Classroom · doi:10.4324/9780203461891_chapter_3 · Cited by 3,544
Bayesian Networks and Decision Graphs
Jensen, F. V. (2001) · Springer eBooks · doi:10.1007/978-1-4757-3502-4 · Cited by 1,734
Point-based value iteration: an anytime algorithm for POMDPs
Pineau, J., Gordon, G., & Thrun, S. (2003) · Cited by 918
Acting Optimally in Partially Observable Stochastic Domains
Cassandra, A. R., Kaelbling, L. P., & Littman, M. L. (1994) · Cited by 558
PARADISE: a framework for evaluating spoken dialogue agents
Walker, M. A., Litman, D. J., Kamm, C. A., & Abella, A. (1997) · Proceedings of the 35th annual meeting on Association for Computational Linguistics - · doi:10.3115/976909.979652 · Cited by 537
Cited by
From Eliza to XiaoIce: Challenges and opportunities with social chatbots
Shum, H.-Y., He, X.-D., & Li, D. (2018) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.1700826 · Cited by 749
“The DM also includes dialog state tracking and policy selection, so that the dialog agent can make more robust decisions (Williams and Young, 2007).”
BackgroundZhao, T., Zhao, R., & Eskenazi, M. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1061 · Cited by 736
Neural Belief Tracker: Data-Driven Dialogue State Tracking
Mrkšić, N., Ó Séaghdha, D., Wen, T.-H., Thomson, B., & Young, S. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1163 · Cited by 459
Self-citationReinforcement learning for clinical decision support in critical care: comprehensive review
Liu, S., See, K. C., Ngiam, K. Y., Celi, L. A., Sun, X., & Feng, M. (2020) · Journal of Medical Internet Research · doi:10.2196/18477 · Cited by 225
Survey on reinforcement learning for language processing
Uc-Cetina, V., Navarro-Guerrero, N., Martin-Gonzalez, A., Weber, C., & Wermter, S. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-022-10205-5 · Cited by 177
Metadata
- Authors
- Williams, J. D., & Young, S.
- Venue
- Computer Speech & Language
- Publisher
- Elsevier BV
- Published
- 2007-04
- Volume
- 21
- Issue
- 2
- Pages
- 393-422
- ISSN
- 0885-2308 (ISSN-L)
- OpenAlex
- W2438667436
- Cited by
- 853
- References
- 55
- FWCI
- 45.54
- Type
- journal article
- Language
- en
- Open access
- closed
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- Williams, J. D., & Young, S. (2007). Partially observable Markov decision processes for spoken dialog systems. Computer Speech & Language. https://doi.org/10.1016/j.csl.2006.06.008
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
