Heather Riley
Papers
2
Total Citations
36
H-Index
2
About
Heather Riley is a leading researcher at the intersection of artificial intelligence, cognitive robotics, and explainable reasoning. Her work focuses on bridging the gap between data-driven deep learning and symbolic logical reasoning, with a particular emphasis on creating transparent, interpretable AI systems. In her highly cited 2019 paper, Riley pioneered methods for integrating non-monotonic logical reasoning with inductive learning for explainable visual question answering—a contribution that has garnered 29 citations and laid the groundwork for more trustworthy AI in complex visual domains. She also made foundational contributions to human-robot collaboration through her 2020 work developing a formal theory of intentions for robotic systems, introducing key principles such as non-procrastination and persistence to enable more natural, goal-directed interactions between humans and machines. Riley’s research addresses critical limitations in modern AI—namely the opacity of deep learning models and the need for machines that can reason about intentions and actions in real-world environments. Her work is essential reading for students and researchers interested in explainable AI, cognitive architectures, and the future of collaborative robotics.
Research Focus
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Top Papers
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