Exploring Wellbeing Outcomes in the Aquatic and Recreation Industry. This project aims to investigate the impact on individual wellbeing through use of public aquatic and recreation centres in Australia. Through the use of mixed methods across multiple locations, the project expects to generate new knowledge on the effect on users of different management and service models for the provision of aquatic and recreational infrastructure. Expected outcomes include a quantifiable measure of social and ....Exploring Wellbeing Outcomes in the Aquatic and Recreation Industry. This project aims to investigate the impact on individual wellbeing through use of public aquatic and recreation centres in Australia. Through the use of mixed methods across multiple locations, the project expects to generate new knowledge on the effect on users of different management and service models for the provision of aquatic and recreational infrastructure. Expected outcomes include a quantifiable measure of social and emotional wellbeing that can be utilised by centre management and government. This will help assessment of best practice for maximising community wellbeing, and can guide investment decisions by state and local government.Read moreRead less
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 an ....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.Read moreRead less