William J. Clancey

Artificial Intelligence · 1977–2019

A researcher in the Science corpus: 12 papers; trusts 42 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 196 citations into it
• Safety, Robustness & Interpretability — 78 citations into it
• Computer Vision and Pattern Recognition — 60 citations into it
• Developmental and Educational Psychology — 33 citations into it
• History and Philosophy of Science — 32 citations into it
• Cognitive Neuroscience — 28 citations into it
• Human-Computer Interaction — 27 citations into it
• Information Systems — 25 citations into it

Whom William J. Clancey trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. William R. Swartout
2. Edward H. Shortliffe
3. Tania Lombrozo
4. Todd Kulesza
5. Robert R. Hoffman
6. Ruth M. J. Byrne
7. Terry Winograd
8. Geoffrey E. Hinton
9. Yash Goyal
10. Denis J. Hilton

Who trusts William J. Clancey overall

1. Robert R. Hoffman
2. Shane T. Mueller
3. Richard W. Southwick
4. Dr. Keith Darlington
5. Gwo-Jen Hwang
6. Johanna D. Moore
7. William R. Swartout
8. Gary Klein
9. Ben Shneiderman
10. M. Rey

Papers from William J. Clancey's perspective

Ordered by how strongly the researchers William J. Clancey trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. ImageNet Large Scale Visual Recognition Challenge
9. Going deeper with convolutions
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI (2019) — 323 citations
• Explaining Explanation, Part 4: A Deep Dive on Deep Nets (2018) — 56 citations
• Methods for Generating Explanations (2005)
• Notes on "Epistemology of a Rule-Based Expert System" (1993)
• Model Construction Operators (1992)
• Situated Cognition: Stepping out of Representational Flatland (1991)
• Methodology for building an intelligent tutoring system (1987)
• From Guidon to Neomycin and Heracles in twenty short lessons (1986)
• Strategic Explanations for a Diagnostic Consultation System (1983)
• The Epistemology of a Rule-Based Expert System - A Framework for Explanation (1981)
• NEOMYCIN: Reconfiguring a Rule-Based Expert System for Application to Teaching (1981)
• Explanation Capabilities of Production-Based Consultation Systems (1977)