Neurosymbolic AI

Alvaro Velasquez, PhD, is a Program Manager at Defense Advanced Research Projects Agency. He is also a Visiting Professor in the Department of Computer Science at the University of Colorado Boulder. Houbing Herbert Song, PhD, is a Tenured Associate Professor, Director of the NSF Center for Aviation Big Data Analytics (Planning), an Associate Director for Leadership of the DOT Transportation Cybersecurity Center for Advanced Research and Education, and Director of Security and Optimization for the Networked Globe Laboratory at the University of Maryland. Pradeep Ravikumar, PhD, is an Assistant Professor in the Department of Computer Science at the University of Texas at Austin. S. Shankar Sastry, PhD, is a Professor of Electrical Engineering and Computer Sciences, Bio-Engineering, and Mechanical Engineering at the University of California, Berkeley. Sandeep Neema, PhD, is a Professor in the Department of Computer Science and the Director of the Institute for Software Integrated Systems at Vanderbilt University.

• List of Contributors xvAbout the Authors xxiPart I Fundamentals 11 What Is Neurosymbolic AI? An Overview and Frontier Problems 3Alvaro Velasquez, Lucas White, Patrick Cooper, Antony Zhao, and Lekai Chen2 Reasoning in Neurosymbolic AI 15Son Tran, Edjard Mota, and Artur d'Avila Garcez3 Neurosymbolic Assurance Using Concept Probes in Foundation Models 69Ramneet Kaur, Anirban Roy, and Susmit Jha4 Toward Assured Autonomy Using Neurosymbolic Components and Systems 89Abhishek Dubey, Taylor T. Johnson, Xenofon Koutsoukos, Baiting Luo, Diego Manzanas Lopez, Miklos Maroti, Ayan Mukhopadhyay, Nicholas Potteiger, Serena Serbinowska, Daniel Stojcsics, Yunuo Zhang, and Gabor Karsai5 Safe Neurosymbolic Learning and Control 119Somil Bansal and Jaime F. Fisac6 Controllable Generation via Locally Constrained Resampling 159Kareem Ahmed, Kai-Wei Chang, and Guy Vanden Broeck7 Tractable and Expressive Generative Modeling with Probabilistic Flow Circuits 183Sahil Sidheekh and Sriraam Natarajan8 Toward Verifiable and ScalableIn-context Fine-tuningin Neurosymbolic AI 223Neel P. Bhatt, Alvaro Velasquez, Zhangyang Wang, and Ufuk TopcuPart II Advanced Topics 2679 Physics-informed Deep Learning 269Nithin Chalapathi, Yiheng Du, Sanjeev Raja, and Aditi S. Krishnapriyan10 Causal Representation Learning 307Burak Varýcý, Chandler Squires, and Pradeep Ravikumar11 Neurosymbolic Computing: Hardware–Software Co-design 347Xiaoxuan Yang, Zhangyang Wang, Miroslav Pajic, Hai "Helen" Li, Yiran Chen, X. Sharon Hu, Chris H. Kim, Shimeng Yu, and Rajit Manohar12 Programmatic Reinforcement Learning 365Swarat ChaudhuriPart III Applications 38113 From Symbolic to Neurosymbolic Information Extraction 383Mihai Surdeanu, Marco A. Valenzuela-Escárcega, GusHahn-Powell, Robert Vacareanu, Gwendolen Herongrove, Enrique Noriega-Atala, Özgün Babur, EmekDemir, and Clayton T. Morrison14 Neurosymbolic AI for Legal AI-TRISM: Trustworthy, Reliable, Interpretable, Safe Models 429Deepa Tilwani, Yash Saxena, Ankur Padia, Srinivasan Parthasarathy, and Manas GaurAcknowledgement 447References 447Index 455