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Beschreibung
This thorough guide is essential for researchers, educators, and professionals interested in the self-assessment and optimization of AI systems. With contributions from experts across disciplines and many examples, it provides comprehensive insights into AI's decision-making processes and ensures safety and reliability in high-stakes applications.
This thorough guide is essential for researchers, educators, and professionals interested in the self-assessment and optimization of AI systems. With contributions from experts across disciplines and many examples, it provides comprehensive insights into AI's decision-making processes and ensures safety and reliability in high-stakes applications.
Inhaltsverzeichnis
Part I. Introduction: 1. Metacognitive AI Hua Wei, Paulo Shakarian, Christian Lebiere, Bruce Draper, Nikhil Krishnaswamy, Sarath Sreedharan and Sergei Nirenburg; Part II. Taxonomy of Metacognitive Approaches: 2. An architectural approach to metacognition Christian Lebiere, Robert Thomson, Andrea Stocco, Mark Orr and Donald Morrison; 3. Metacognitive AI through error detection and correction rules Bowen Xi and Paulo Shakarian; 4. Mutual trust in human-AI teams relies on metacognition Sergei Nirenburg, Marjorie McShane and Thomas M. Ferguson; Part III. Neuro-Symbolic Models in AI: 5. Learning where and when to reason in neuro-symbolic inference Christina Cornelio; 6. Assessment of competency of learning agents via inference of temporal logic formulas Zhe Xu, Nasim Baharisangari, Jean-Raphaël Gaglione and Ufuk Topcu; Part IV. Metacognition with LLMs: 7. Metacognitive intervention for accountable LLMs through sparsity Tianlong Chen; 8. Metacognitive insights into ChatGPT's arithmetic reasoning Noel Ngu, Paulo Shakarian, Abhinav Koyyalamudi and Lakshmivihari Mareedu; Part V. Metacognition in Learning Agents: 9. Uncertainty quantification's role in metacognition Gavin Strunk; 10. The role of predictive uncertainty and diversity in embodied AI and robot learning Ransalu Senanayake; Part VI. Assured Machine Learning in High-Stakes Domains: 11. Towards certifiably trustworthy deep learning at scale Linyi Li; 12. Metacognition with neural network verification and repair using Veritex Xiaodong Yang, Tomoya Yamaguchi, Bardh Hoxha, Danil Prokhorov and Taylor T. Johnson; Part VII. Metacognition as a Solution to Handle Failure: 13. Reasoning about anomalous object interaction using plan failure as a metacognitive trigger Nikhil Krishnaswamy; 14. Tractable probabilistic reasoning for trustworthy AI YooJung Choi; Part VIII. Applications of Metacognitive AI: 15. Robust and compositional concept grounding for image generative AI Yezhou Yang; 16. mLINK: Machine learning integration with network and knowledge Sergei Chuprov, Raman Zatsarenko and Leon Reznik; 17. Military applications of artificial intelligence metacognition Bonnie Johnson.
Details
| Erscheinungsjahr: | 2025 |
|---|---|
| Fachbereich: | Programmiersprachen |
| Genre: | Importe, Informatik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Buch |
| ISBN-13: | 9781009522458 |
| ISBN-10: | 1009522450 |
| Sprache: | Englisch |
| Einband: | Gebunden |
| Redaktion: |
Shakarian, Paulo
Wei, Hua |
| Hersteller: | Cambridge University Press |
| Verantwortliche Person für die EU: | Libri GmbH, Europaallee 1, D-36244 Bad Hersfeld, gpsr@libri.de |
| Maße: | 235 x 157 x 21 mm |
| Von/Mit: | Paulo Shakarian (u. a.) |
| Erscheinungsdatum: | 21.08.2025 |
| Gewicht: | 0,599 kg |