Artificial Intelligence for Decision Making: Dynamic Planning under Fundamental Uncertainty

Project Member(s): Bishop, A.

Funding or Partner Organisation: NSW Department of Industry (NSW Defence Innovation Network)
NSW Department of Industry (NSW Defence Innovation Network)

Start year: 2021

Summary: Broadly, here we address the challenge of taking existing machine learning methods that make strong assumptions about stationarity in dynamical processes, and adapting these methods and/or understanding how existing methods perform in situations where that assumption cannot be made.

Publications:

Bishop, AN & Del Moral, P 2023, 'Robust Kalman and Bayesian Set-Valued Filtering and Model Validation for Linear Stochastic Systems', SIAM/ASA Journal on Uncertainty Quantification, vol. 11, no. 2, pp. 389-425.
View/Download from: Publisher's site

Bishop, AN & Bonilla, EV 1970, 'Recurrent Neural Networks and Universal Approximation of Bayesian Filters', Proceedings of Machine Learning Research, pp. 6956-6967.

FOR Codes: Emerging Defence Technologies, Applied mathematics, Numerical and computational mathematics

Acknowledgement of Country

UTS acknowledges the Gadigal people of the Eora Nation, the Boorooberongal people of the Dharug Nation, the Bidiagal people and the Gamaygal people, upon whose ancestral lands our university stands. We would also like to pay respect to the Elders both past and present, acknowledging them as the traditional custodians of knowledge for these lands.