Agents in the Long Game of AI

Three experts offer a novel approach to hybrid AI—which combines machine learning with knowledge-based processing—aimed at developing trustworthy agent collaborators.

The vast majority of current AI relies wholly on machine learning (ML). However, the past thirty years of effort in this paradigm have shown that, despite the many things that machine learning can achieve, it is not an all-purpose solution to building human-like intelligent systems. One hope for overcoming this limitation is hybrid AI: that is, AI that combines machine learning with knowledge-based processing. In Agents in the Long Game of AI, Marjorie McShane, Sergei Nirenburg, and Jesse English present recent advances in hybrid AI with special emphases on content-centric computational cognitive modeling, explainability, and development methodologies.

At present, hybridization typically involves sprinkling knowledge into a machine learning black box. The authors, by contrast, argue that hybridization will be best achieved in the opposite way: by building agents within a cognitive architecture and then integrating judiciously selected machine learning results. This approach leverages the power of machine learning without sacrificing the kind of explainability that will foster society’s trust in AI. This book shows how we can develop trustworthy agent collaborators of a type not being addressed by the “ML alone” or “ML sprinkled by knowledge” paradigms—and why it is imperative to do so.Contents
Chapter 1. Setting the Stage
Chapter 2. Content-Centric Cognitive Modeling
Chapter 3. Knowledge Bases
Chapter 4. Language Understanding and Generation
Chapter 5. The Trajectory of Microtheory Development: The Example of Coreference
Chapter 6. Dialog as Perception, Reasoning and Action
Chapter 7. Learning
Chapter 8. Explanation
Chapter 9. Knowledge Acquisition
Chapter 10. Disrupting the Dominant Paradigm
Notes
References
Index“Agents in the Long Game of AI is a significant and timely contribution to artificial intelligence. An essential read for AI researchers, cognitive scientists, and technologists, this book charts a visionary course for the future of AI.”
—Shlomo Argamon, Computer Science Department, Illinois Institute of Technology

“The rise of AI based on large language models has led to considerable excitement. But is that approach the best for creating human-like AI systems? McShane and her colleagues argue instead for knowledge-rich cognitive architecture, where language is intertwined with formal representations and reasoning. Unlike large language models, which are trained “at the factory” and cannot easily be updated, their architecture can adapt to people incrementally, a characteristic we expect from human collaborators. This book is an interesting exploration of a possible future direction for AI.”
—Kenneth D Forbus, Walter P. Murphy Professor of Computer Science and Professor of Education, Northwestern UniversityMarjorie McShane and Sergei Nirenburg are Professors in the Cognitive Science Department and Co-Directors of the Language-Endowed Intelligent Agents Lab at Rensselaer Polytechnic Institute.
Jesse English is Senior Research Scientist in the Language-Endowed Intelligent Agents Lab at Rensselaer Polytechnic Institute.