Closing the gap: Improved bounds on optimal POMDP solutions

POMDP algorithms have made significant progress in recent years by allowing practitioners to find good solutions to increasingly large problems. Most approaches (including point-based and policy iteration techniques) operate by refining a lower bound of the optimal value function. Several approaches (e.g., HSVI2, SARSOP, grid-based approaches and online forward search) also refine an upper bound. However, approximating the optimal value function by an upper bound is computationally expensive and therefore tightness is often sacrificed to improve efficiency (e.g., sawtooth approximation). In this paper, we describe a new approach to efficiently compute tighter bounds by i) conducting a prioritized breadth first search over the reachable beliefs, ii) propagating upper bound improvements with an augmented POMDP and iii) using exact linear programming (instead of the sawtooth approximation) for upper bound interpolation. As a result, we can represent the bounds more compactly and significantly reduce the gap between upper and lower bounds on several benchmark problems. Copyright © 2011, Association for the Advancement of Artificial Intelligence. All rights reserved.

复合垂直流人工湿地在奥林匹克森林公园龙型水系的应用

介绍了北京奥林匹克森林公园人工湿地系统的设计,跟踪调查了复合垂直流人工湿地在龙型水系水质改善中的功能和效果。复合垂直流人工湿地系统占地面积为41 500 m2,处理水量为2 600 m3/d的污水处理厂尾水(再生水)和20 000 m3/d的主湖循环水。一年多的运行表明,该系统与其他生态工程的协同作用有效地改善了公园内的龙型水系水质,特别是在奥运期间,在保证了奥运主湖水质良好的同时,为运动员和观众提供了优美的休息环境,取得了良好的生态、环境、经济和文化等综合效益。