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.
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.
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.
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