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·
Professor Maurice Pagnucco
,
Research UNSW
2020–
·
Deputy Dean (Education) of the Faculty of Engineering
,
UNSW
Publications
(195)
Dynamic belief revision operators ✩
2003
138
cited
Severe Withdrawal (and Recovery)
Journal of Philosophical Logic · 1999
113
cited
Iterated belief change in the situation calculus
Artificial Intelligence · 2000
105
cited
Application of cognitive robotics in disassembly of products
2013
98
cited
Graph-based Spatial Transformer with Memory Replay for Multi-future Pedestrian Trajectory Prediction
Computer Vision and Pattern Recognition · 2022
78
cited
Learning and revision in cognitive robotics disassembly automation
2015
71
cited
The Role of Abductive Reasoning within the Process of Belief Revision
1996
71
cited
Basic behaviour control of the vision‐based cognitive robotic disassembly automation
2013
62
cited
Colour adaptive generative networks for stain normalisation of histopathology images
Medical Image Anal. · 2022
44
cited
Screw detection for disassembly of electronic waste using reasoning and re-training of a deep learning model
2021
44
cited
SAC-Net: Learning with weak and noisy labels in histopathology image segmentation
Medical Image Anal. · 2023
41
cited
Changing conditional beliefs unconditionally
1996
39
cited
Explainability in Graph Neural Networks: An Experimental Survey
arXiv.org · 2022
39
cited
Legolog : Inexpensive Experiments in Cognitive Robotics
2000
39
cited
Using Modern Technologies to Capture and Share Indigenous Astronomical Knowledge
2014
36
cited
Discriminative Latent Semantic Graph for Video Captioning
ACM Multimedia · 2021
33
cited
GrainSpace: A Large-scale Dataset for Fine-grained and Domain-adaptive Recognition of Cereal Grains
Computer Vision and Pattern Recognition · 2022
33
cited
Automated generation and execution of disassembly actions
Robotics Comput. Integr. Manuf. · 2021
32
cited
Vision-Based Multi-Future Trajectory Prediction: A Survey
IEEE Transactions on Neural Networks and Learning Systems · 2023
32
cited
Evaluating Sequential Single-Item Auctions for Dynamic Task Allocation
Australasian Conference on Artificial Intelligence · 2010
31
cited
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