The DOI matches its OpenAlex record; 28 of the 28 references it cites were found in a registry or an index.
Point-Based Value Iteration for Continuous POMDPs
Open accessAbstract
We propose a novel approach to optimize Partially Observable Markov Decisions Processes (POMDPs) defined on continuous spaces. To date, most algorithms for model-based POMDPs are restricted to discrete states, actions, and observations, but many real-world problems such as, for instance, robot navigation, are naturally defined on continuous spaces. In this work, we demonstrate that the value function for continuous POMDPs is convex in the beliefs over continuous state spaces, and piecewise-linear convex for the particular case of discrete observations and actions but still continuous states. We also demonstrate that continuous Bellman backups are contracting and isotonic ensuring the monotonic convergence of value-iteration algorithms. Relying on those properties, we extend the PERSEUS algorithm, originally developed for discrete POMDPs, to work in continuous state spaces by representing the observation, transition, and reward models using Gaussian mixtures, and the beliefs using Gaussian mixtures or particle sets. With these representations, the integrals that appear in the Bellman backup can be computed in closed form and, therefore, the algorithm is computationally feasible. Finally, we further extend PERSEUS to deal with continuous action and observation sets by designing effective sampling approaches.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Shannon, R. D. (1976) · Acta Crystallographica Section A · doi:10.1107/s0567739476001551 · Cited by 66,145
Bellman, R. E. (2010) · doi:10.1515/9781400835386 · Cited by 14,588
Markov decision processes: Discrete stochastic dynamic programming
Hazeghi, K., & Puterman, M. L. (1995) · Journal of the American Statistical Association · doi:10.2307/2291177 · Cited by 8,037
Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches
Aamodt, A., & Plaza, E. (1994) · AI Communications · doi:10.3233/aic-1994-7104 · Cited by 5,624
CONDENSATION—Conditional Density Propagation for Visual Tracking
Isard, M., & Blake, A. (1998) · International Journal of Computer Vision · doi:10.1023/a:1008078328650 · Cited by 4,886
The Embodied Mind: Cognitive Science and Human Experience
Varela, F. J., Thompson, E., & Rosch, E. (2017) · doi:10.7551/mitpress/9780262529365.001.0001 · Cited by 2,449
Affordance, conventions, and design
Norman, D. A. (1999) · Interactions · doi:10.1145/301153.301168 · Cited by 2,040
Seabird conservation status, threats and priority actions: a global assessment
CROXALL, J. P., BUTCHART, S. H. M., LASCELLES, B., STATTERSFIELD, A. J., SULLIVAN, B., SYMES, A., & TAYLOR, P. (2012) · Bird Conservation International · doi:10.1017/s0959270912000020 · Cited by 1,176
HOW MANY SPECIES OF ALGAE ARE THERE?
Guiry, M. D. (2012) · Journal of Phycology · doi:10.1111/j.1529-8817.2012.01222.x · Cited by 1,002
Smolensky, P. (1990) · Artificial Intelligence · doi:10.1016/0004-3702(90)90007-m · Cited by 959 · cited 1×
“Smolensky’s tensor product is one of the simplest variants of VSA (Smolensky, 1990).”
Cited by
Data preprocessing in predictive data mining
Alexandropoulos, S.-A. N., Kotsiantis, S. B., & Vrahatis, M. N. (2019) · The Knowledge Engineering Review · doi:10.1017/s026988891800036x · Cited by 209
Ke, X., Bernal, S. A., Provis, J. L., & Lothenbach, B. (2020) · Cement and Concrete Research · doi:10.1016/j.cemconres.2020.106158 · Cited by 117
Hill, B. L., Brown, R., Gabel, E., Rakocz, N., Lee, C., Cannesson, M., Baldi, P., Olde Loohuis, L., Johnson, R., Jew, B., et al. (2019) · British Journal of Anaesthesia · doi:10.1016/j.bja.2019.07.030 · Cited by 103
Predatory and Exploitative Behaviour in Academic Publishing: An Assessment
Teixeira da Silva, J. A., Dobránszki, J., Tsigaris, P., & Al-Khatib, A. (2019) · The Journal of Academic Librarianship · doi:10.1016/j.acalib.2019.102071 · Cited by 77
Patil, S., Kahn, G., Laskey, M., Schulman, J., Goldberg, K., & Abbeel, P. (2015) · Springer Tracts in Advanced Robotics · doi:10.1007/978-3-319-16595-0_30 · Cited by 72
Metadata
- Authors
- Porta, J. M., Vlassis, N. A., Spaan, M. T. J., & Poupart, P.
- Venue
- DIGITAL.CSIC (Spanish National Research Council (CSIC))
- Publisher
- Consejo Superior de Investigaciones Científicas
- Published
- 2006-12-01
- Volume
- 7
- Issue
- 83
- Pages
- 2329-2367
- OpenAlex
- W2144088174
- Cited by
- 234
- References
- 28
- FWCI
- 10.33
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Point-Based Value Iteration for Continuous POMDPs. https://app.cochat.ai/papers/10.13039/501100000780
- The paper
- Porta, J. M., Vlassis, N. A., Spaan, M. T. J., & Poupart, P. (2006). Point-Based Value Iteration for Continuous POMDPs. DIGITAL.CSIC (Spanish National Research Council (CSIC)). https://doi.org/10.13039/501100000780
Data sources
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.
