Research Grant
[Cite as https://purl.org/au-research/grants/arc/DP200101049]Researchers: Hanna Kurniawati (Chief Investigator) , Jerzy Filar (Chief Investigator) , Nan Ye (Chief Investigator) , Professor Dirk Kroese (Chief Investigator) , Martin Bohner (Partner Investigator)
Brief description Partially Observable MDPs, Monte Carlo Methods, and Sustainable Fisheries. Partially Observable Markov Decision Processes (POMDPs) provide a general mathematical framework for sequential decision making under uncertainty. However, solving POMDPs effectively under realistic assumptions remains a challenging problem. This project aims to develop new efficient Monte Carlo algorithms to significantly advance the application of POMDPs to real-world decision problems involving complex action spaces and system dynamics. Both theoretical and algorithmic approaches will be applied to sustainable fishery management --- an important problem for Australia and an ideal context for POMDPs. The project will advance research in artificial intelligence, dynamical systems, and fishery operations, and benefit the national economy.
Funding Amount $365,000
Funding Scheme Discovery Projects
- PURL : https://purl.org/au-research/grants/arc/DP200101049
- ARC : DP200101049