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A novel approach to hybrid AI aimed at developing trustworthy agent collaborators. The vast majority of current AI relies wholly on machine learning (ML). However, the past 30 years of effort in this paradigm have shown that, despite the many things that ML 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 ML with knowledge-based processing. In At present hybridization typically involves sprinkling knowledge into an ML 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 ML results. This approach leverages the power of ML 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.
List of contents
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
About the author
Marjorie 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.
