Measuring the State-Observation-Gap in POMDPs: An Exploration of Observation Confidence and Weighting Algorithms
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-34111-3_13
Reference20 articles.
1. Pajarinen, J., Lundell, J., Kyrki, V.: POMDP planning under object composition uncertainty: Application to robotic manipulation. IEEE Trans. Robotics (2022)
2. Zhang, C., et al.: Traffic Mirror-Aware POMDP behavior planning for autonomous urban driving. In: 2022 IEEE Intelligent Vehicles Symposium (IV). IEEE (2022)
3. Singh, G., Roy, R.N., Chanel, C.P.C.: Pomdp-based adaptive interaction through physiological computing (2022)
4. Chadès, I., Pascal, L.V., Nicol, S., Fletcher, C.S., Ferrer-Mestres, J.: A primer on partially observable Markov decision processes (POMDPs). Methods Ecol. Evol. 12(11), 2058–2072 (2021)
5. Åström, K.J.: Optimal control of Markov processes with incomplete state information. J. Math. Anal. Appl. 10(1), 174–205 (1965)
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