Advances in approximate and adaptive dynamic programming

根据这一建议开展的研究将推进理论和应用。它将专注于不确定条件下序列决策中的复杂问题，特别关注解决大问题的改进方法(近似动态规划)和决策过程中系统参数的学习方法(自适应动态规划)。对于近似动态规划，它将为近似解以及几个理论和实践结果提供新的界限。近似动态规划在医疗保健管理和在线拍卖中的新应用将补充理论进步，并进一步深入了解使用这些方法的实际方面。*自适应动态规划方法将应用于前列腺癌治疗和通过互联网拍卖进行销售的问题。这项研究在医疗保健问题中的应用解决了与有效利用稀缺资源有关的重要管理挑战。这一领域的创新将显著影响加拿大人今天面临的头号问题：及时获得高质量的医疗服务。

Research carried out under this proposal will advance theory and application.  It will focus on complex problems in sequential decision making under uncertainty paying special attention to advancing methods for solving large problems (approximate dynamic programming) and methods for learning system parameters in the course of the decision making process (adaptive dynamic programming).  For approximate dynamic programming, it will provide new bounds on approximate solutions along with several theoretical and practical results.  Novel applications of approximate dynamic programming to health care management and online auctions will complement the theoretical advances and provide further insight into the practical aspects of using these methods.  Adaptive dynamic programming methods will be applied to problems in prostate cancer treatment and selling through Internet auctions.   The applications of this research to healthcare issues address important management challenges concerning the efficient use of scarce resources. Innovations in this area will significantly impact the number one issue facing Canadians today; timely access to quality medical care.