Partially Observable MDPs, Monte Carlo Methods, and Sustainable Fisheries

部分可观测马尔可夫决策过程（POMDPs）为不确定性下的序列决策提供了一个通用的数学框架。然而，在现实的假设下有效地解决POMDPs仍然是一个具有挑战性的问题。该项目旨在开发新的高效蒙特卡罗算法，以显着推进POMDPs在涉及复杂行动空间和系统动力学的现实世界决策问题中的应用。理论和算法的方法将应用于可持续渔业管理-澳大利亚的一个重要问题和POMDPs的理想背景。该项目将促进人工智能、动力系统和渔业作业的研究，并使国民经济受益。

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.