Results 31 to 40 of about 35,199 (318)
Decision-making models on perceptual uncertainty with distributional reinforcement learning
Green Energy and Intelligent Transportation, 2023 Decision-making for autonomous vehicles in the presence of obstacle occlusions is difficult because the lack of accurate information affects the judgment.
Shuyuan Xu +4 more
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Partially Observable Markov Decision Processes [PDF]
, 2012 For reinforcement learning in environments in which an agent has access to a reliable state signal, methods based on the Markov decision process (MDP) have had many successes. In many problem domains, however, an agent suffers from limited sensing capabilities that preclude it from recovering a Markovian state signal from its perceptions. Extending the
openaire +2 more sources
Blackwell optimality in Markov decision processes with partial observation [PDF]
The Annals of Statistics, 2002 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Rosenberg, Dinah +2 more
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Technical Report: Distribution Temporal Logic: Combining Correctness with Quality of Estimation [PDF]
, 2013 We present a new temporal logic called Distribution Temporal Logic (DTL) defined over predicates of belief states and hidden states of partially observable systems.
Belta, Calin +2 more
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International Journal of Advanced Robotic Systems, 2018 In this article, we study the ground moving target tracking problem for a fixed-wing unmanned aerial vehicle equipped with a radar. This problem is formulated in a partially observable Markov process framework, which contains the following two parts: in ...
Yunyun Zhao +3 more
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Reinforcement Learning-Based Detection for State Estimation Under False Data Injection
IEEE Access, 2021 We consider the problem of network security under false data injection attacks over wireless sensor networks.To resist the attacks which can inject false data into communication channels according to a certain probability, we formulate the online attack ...
Weiliang Jiang +5 more
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Nonapproximability Results for Partially Observable Markov Decision Processes
Journal of Artificial Intelligence Research, 2001 We show that for several variations of partially observable Markov decision processes, polynomial-time algorithms for finding control policies are unlikely to or simply don't have guarantees of finding policies within a constant factor or a constant summand of optimal. Here ``unlikely'' means ``unless some complexity classes collapse,''
Lusena, C., Goldsmith, J., Mundhenk, M.
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Sensors, 2022 An intelligent land vehicle utilizes onboard sensors to acquire observed states at a disorderly intersection. However, partial observation of the environment occurs due to sensor noise. This causes decision failure easily.
Lingli Yu +3 more
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Recursively-Constrained Partially Observable Markov Decision Processes
, 2023 Many sequential decision problems involve optimizing one objective function while imposing constraints on other objectives. Constrained Partially Observable Markov Decision Processes (C-POMDP) model this case with transition uncertainty and partial observability.
Ho, Qi Heng +7 more
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SystemsAs network systems become larger and more complex, there is an increasing focus on how to verify the security of systems that are at risk of being attacked. Automated penetration testing is one of the effective ways to achieve this. Uncertainty caused by
Xiaojian Liu +3 more
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