https://arxiv.org/abs/2502.05244 Skip to main content Cornell University We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate arxiv logo > cs > arXiv:2502.05244 [ ] Help | Advanced Search [All fields ] Search arXiv logo Cornell University Logo [ ] GO quick links * Login * Help Pages * About Computer Science > Artificial Intelligence arXiv:2502.05244 (cs) [Submitted on 7 Feb 2025] Title:Probabilistic Artificial Intelligence Authors:Andreas Krause, Jonas Hubotter View a PDF of the paper titled Probabilistic Artificial Intelligence, by Andreas Krause and 1 other authors View PDF Abstract:Artificial intelligence commonly refers to the science and engineering of artificial systems that can carry out tasks generally associated with requiring aspects of human intelligence, such as playing games, translating languages, and driving cars. In recent years, there have been exciting advances in learning-based, data-driven approaches towards AI, and machine learning and deep learning have enabled computer systems to perceive the world in unprecedented ways. Reinforcement learning has enabled breakthroughs in complex games such as Go and challenging robotics tasks such as quadrupedal locomotion. A key aspect of intelligence is to not only make predictions, but reason about the uncertainty in these predictions, and to consider this uncertainty when making decisions. This is what this manuscript on "Probabilistic Artificial Intelligence" is about. The first part covers probabilistic approaches to machine learning. We discuss the differentiation between "epistemic" uncertainty due to lack of data and "aleatoric" uncertainty, which is irreducible and stems, e.g., from noisy observations and outcomes. We discuss concrete approaches towards probabilistic inference and modern approaches to efficient approximate inference. The second part of the manuscript is about taking uncertainty into account in sequential decision tasks. We consider active learning and Bayesian optimization -- approaches that collect data by proposing experiments that are informative for reducing the epistemic uncertainty. We then consider reinforcement learning and modern deep RL approaches that use neural network function approximation. We close by discussing modern approaches in model-based RL, which harness epistemic and aleatoric uncertainty to guide exploration, while also reasoning about safety. Subjects: Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2502.05244 [cs.AI] (or arXiv:2502.05244v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2502.05244 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Jonas Hubotter [view email] [v1] Fri, 7 Feb 2025 14:29:07 UTC (4,157 KB) Full-text links: Access Paper: View a PDF of the paper titled Probabilistic Artificial Intelligence, by Andreas Krause and 1 other authors * View PDF * TeX Source * Other Formats license icon view license Current browse context: cs.AI < prev | next > new | recent | 2025-02 Change to browse by: cs cs.LG References & Citations * NASA ADS * Google Scholar * Semantic Scholar a export BibTeX citation Loading... BibTeX formatted citation x [loading... ] Data provided by: Bookmark BibSonomy logo Reddit logo (*) Bibliographic Tools Bibliographic and Citation Tools [ ] Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) 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