Metacognitive Artificial Intelligence

Paulo Shakarian is the KG Tan Endowed Professor of Artificial Intelligence at Syracuse University. He has made notable contributions in the areas of logic programming, neurosymbolic AI, security, and data mining. His academic accomplishments include four best-paper awards, over 100 peer-reviewed articles, over 10 issued patents, and 8 published books. He has been featured on CNN and in 'The Economist.' Hua Wei is Assistant Professor at the School of Computing and Augmented Intelligence at Arizona State University. He specializes in data mining, artificial intelligence, and machine learning. His work has been awarded multiple best paper awards and his research has been funded by agencies including the National Science Foundation and the U.S. Department of Energy.

• 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.

'This book offers a fascinating exploration of the astounding relationship between metacognition and AI. It provides readers with a comprehensive understanding of how AI systems can be designed not only to make accurate predictions but also to learn from their mistakes and improve over time. The authors explore various methods for enhancing trust in AI models by incorporating aspects of human cognitive processes, providing practical insights for building more reliable and transparent AI technologies.' Todd C. Hughes, Scientific Systems Chief Innovation Officer