The approach includes description of the proposed probabilistic models, optimization methods for rationale actions and incremental algorithms for solving the problems of supporting decision-making on the base of monitored data and rationale a robot actions in uncertainty conditions. The approach means ...
Subsequently the driving decision is made and sent to the trajectory planning module. In order to reflect the greater risks of the truck to other surrounding vehicles, the aggressiveness index (AI) is proposed and quantified to infer the asymmetrical risk level of lane-change maneuver. In the ...
Making design decisions is characterized by a high degree of uncertainty, especially in the early phase of the product development process, when little inf
In this paper, we review several effective approaches: Coulomb counting, Open Circuit Voltage (OCV) and Kalman Filter method for performing the SoC estimation; then we propose Artificial Intelligence (AI) approach that can be efficiently used to precisely determine the SoC estimation for the smart ...
A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory. This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeli...
Generating credible probability (or confidence) of prediction is crucial in clinical practice and medical research when applying artificial intelligence (AI) to healthcare. Reliable probability outputs are critical for informed decision-making, stratifying patients with different risk levels, and enabling cl...
The Internet of Things (IoT) is one of the driving forces behind Industry 4.0 and has the potential to improve the entire value chain, especially in the co
where l1 and l2 are losses caused by making the decision C1 or C2. The decision boundary denned by the above decision rule is p1(x)=(l2pa2/l1pa1)p2(x) It could be a nonlinear decision surface of arbitrary complexity, which is taken into account when only two categories are present in...
In this work, we introduce a novel machine learning approach that harnesses Convolutional Neural Networks (CNNs) and Diffusion Models, trained on the CAMELS simulation suite, to bridge the gap between computationally inexpensive dark matter simulations and the galaxy distributions of more costly ...
A Bayesian approach for learning and planning in partially observable Markov decision processes. J. Mach. Learn. Res. 12, 1729–1770 (2011). MathSciNet MATH Google Scholar Jaulmes, R., Pineau, J. & Precup, D. Active learning in partially observable Markov decision processes. In European ...