排序方式： 共有78条查询结果，搜索用时 15 毫秒
41.
一种动态不确定性环境中的持续规划系统 总被引：5，自引：1，他引：5
规划是人工智能研究的一个重要方向,具有极其广泛的应用背景．近年来,研究重点已经转移到动态不确定性环境中的规划问题．该文将部分可观察马尔可夫决策过程(POMDP)和过程性推理系统(PRS)的优点相结合,提出一种对动态不确定环境具有更全面适应能力的持续规划系统——POMDPRS．该系统利用PRS的持续规划机制,交叉地进行规划与执行,在一定条件下提高了动态环境中POMDP决策的效率;另一方面,用POMDP的概率分布信念模型和极大效用原理替代PRS的一阶逻辑信念表示和计划选择机制,大大增强了处理环境不确定性的能力． 相似文献
42.
In highly flexible and integrated manufacturing systems, such as semiconductor fabs, strong interactions between the equipment condition, operations executed on the various machines and the outgoing product quality necessitate integrated decision making in the domains of maintenance scheduling and production operations. Furthermore, in highly complex manufacturing equipment, the underlying condition is not directly observable and can only be inferred probabilistically from the available sensor readings. In order to deal with interactions between maintenance and production operations in Flexible Manufacturing Systems (FMSs) in which equipment conditions are not perfectly observable, we propose in this paper a decision-making method based on a Partially Observable Markov Decision Processes (POMDP's), yielding an integrated policy in the realms of maintenance scheduling and production sequencing. Optimization was pursued using a metaheuristic method that used the results of discrete-event simulations of the underlying manufacturing system. The new approach is demonstrated in simulations of a generic semiconductor manufacturing cluster tool. The results showed that, regardless of uncertainties in the knowledge of actual equipment conditions, jointly making maintenance and production sequencing decisions consistently outperforms the current practice of making these decisions separately. 相似文献
43.
Edwin K. P. Chong Christopher M. Kreucher Alfred O. Hero III 《Discrete Event Dynamic Systems》2009,19(3):377-422
Adaptive sensing involves actively managing sensor resources to achieve a sensing task, such as object detection, classification,
and tracking, and represents a promising direction for new applications of discrete event system methods. We describe an approach
to adaptive sensing based on approximately solving a partially observable Markov decision process (POMDP) formulation of the
problem. Such approximations are necessary because of the very large state space involved in practical adaptive sensing problems,
precluding exact computation of optimal solutions. We review the theory of POMDPs and show how the theory applies to adaptive
sensing problems. We then describe a variety of approximation methods, with examples to illustrate their application in adaptive
sensing. The examples also demonstrate the gains that are possible from nonmyopic methods relative to myopic methods, and
highlight some insights into the dependence of such gains on the sensing resources and environment.
Edwin K. P. Chong received the BE(Hons) degree with First Class Honors from the University of Adelaide, South Australia, in 1987; and the MA and PhD degrees in 1989 and 1991, respectively, both from Princeton University, where he held an IBM Fellowship. He joined the School of Electrical and Computer Engineering at Purdue University in 1991, where he was named a University Faculty Scholar in 1999, and was promoted to Professor in 2001. Since August 2001, he has been a Professor of Electrical and Computer Engineering and a Professor of Mathematics at Colorado State University. His research interests span the areas of communication and sensor networks, stochastic modeling and control, and optimization methods. He coauthored the recent best-selling book, An Introduction to Optimization, 3rd Edition, Wiley-Interscience, 2008. He is currently on the editorial board of the IEEE Transactions on Automatic Control, Computer Networks, Journal of Control Science and Engineering, and IEEE Expert Now. He is a Fellow of the IEEE, and served as an IEEE Control Systems Society Distinguished Lecturer. He received the NSF CAREER Award in 1995 and the ASEE Frederick Emmons Terman Award in 1998. He was a co-recipient of the 2004 Best Paper Award for a paper in the journal Computer Networks. He has served as Principal Investigator for numerous funded projects from NSF, DARPA, and other funding agencies. Christopher M. Kreucher received the BS, MS, and PhD degrees in Electrical Engineering from the University of Michigan in 1997, 1998, and 2005, respectively. He is currently a Senior Systems Engineer at Integrity Applications Incorporated in Ann Arbor, Michigan. His current research interests include nonlinear filtering (specifically particle filtering), Bayesian methods of fusion and multitarget tracking, self localization, information theoretic sensor management, and distributed swarm management. Alfred O. Hero III received the BS (summa cum laude) from Boston University (1980) and the PhD from Princeton University (1984), both in Electrical Engineering. Since 1984 he has been with the University of Michigan, Ann Arbor, where he is a Professor in the Department of Electrical Engineering and Computer Science and, by courtesy, in the Department of Biomedical Engineering and the Department of Statistics. He has held visiting positions at Massachusetts Institute of Technology (2006), Boston University, I3S University of Nice, Sophia-Antipolis, France (2001), Ecole Normale Superieure de Lyon (1999), Ecole Nationale Superieure des Telecommunications, Paris (1999), Scientific Research Labs of the Ford Motor Company, Dearborn, Michigan (1993), Ecole Nationale Superieure des Techniques Avancees (ENSTA), Ecole Superieure d’Electricite, Paris (1990), and M.I.T. Lincoln Laboratory (1987–1989). His recent research interests have been in areas including: inference for sensor networks, adaptive sensing, bioinformatics, inverse problems. and statistical signal and image processing. He is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE), a member of Tau Beta Pi, the American Statistical Association (ASA), the Society for Industrial and Applied Mathematics (SIAM), and the US National Commission (Commission C) of the International Union of Radio Science (URSI). He has received a IEEE Signal Processing Society Meritorious Service Award (1998), IEEE Signal Processing Society Best Paper Award (1998), a IEEE Third Millenium Medal and a 2002 IEEE Signal Processing Society Distinguished Lecturership. He was President of the IEEE Signal Processing Society (2006–2007) and during his term served on the TAB Periodicals Committee (2006). He was a member of the IEEE TAB Society Review Committee (2008) and is Director-elect of IEEE for Division IX (2009). 相似文献
| Alfred O. Hero IIIEmail: |
Edwin K. P. Chong received the BE(Hons) degree with First Class Honors from the University of Adelaide, South Australia, in 1987; and the MA and PhD degrees in 1989 and 1991, respectively, both from Princeton University, where he held an IBM Fellowship. He joined the School of Electrical and Computer Engineering at Purdue University in 1991, where he was named a University Faculty Scholar in 1999, and was promoted to Professor in 2001. Since August 2001, he has been a Professor of Electrical and Computer Engineering and a Professor of Mathematics at Colorado State University. His research interests span the areas of communication and sensor networks, stochastic modeling and control, and optimization methods. He coauthored the recent best-selling book, An Introduction to Optimization, 3rd Edition, Wiley-Interscience, 2008. He is currently on the editorial board of the IEEE Transactions on Automatic Control, Computer Networks, Journal of Control Science and Engineering, and IEEE Expert Now. He is a Fellow of the IEEE, and served as an IEEE Control Systems Society Distinguished Lecturer. He received the NSF CAREER Award in 1995 and the ASEE Frederick Emmons Terman Award in 1998. He was a co-recipient of the 2004 Best Paper Award for a paper in the journal Computer Networks. He has served as Principal Investigator for numerous funded projects from NSF, DARPA, and other funding agencies. Christopher M. Kreucher received the BS, MS, and PhD degrees in Electrical Engineering from the University of Michigan in 1997, 1998, and 2005, respectively. He is currently a Senior Systems Engineer at Integrity Applications Incorporated in Ann Arbor, Michigan. His current research interests include nonlinear filtering (specifically particle filtering), Bayesian methods of fusion and multitarget tracking, self localization, information theoretic sensor management, and distributed swarm management. Alfred O. Hero III received the BS (summa cum laude) from Boston University (1980) and the PhD from Princeton University (1984), both in Electrical Engineering. Since 1984 he has been with the University of Michigan, Ann Arbor, where he is a Professor in the Department of Electrical Engineering and Computer Science and, by courtesy, in the Department of Biomedical Engineering and the Department of Statistics. He has held visiting positions at Massachusetts Institute of Technology (2006), Boston University, I3S University of Nice, Sophia-Antipolis, France (2001), Ecole Normale Superieure de Lyon (1999), Ecole Nationale Superieure des Telecommunications, Paris (1999), Scientific Research Labs of the Ford Motor Company, Dearborn, Michigan (1993), Ecole Nationale Superieure des Techniques Avancees (ENSTA), Ecole Superieure d’Electricite, Paris (1990), and M.I.T. Lincoln Laboratory (1987–1989). His recent research interests have been in areas including: inference for sensor networks, adaptive sensing, bioinformatics, inverse problems. and statistical signal and image processing. He is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE), a member of Tau Beta Pi, the American Statistical Association (ASA), the Society for Industrial and Applied Mathematics (SIAM), and the US National Commission (Commission C) of the International Union of Radio Science (URSI). He has received a IEEE Signal Processing Society Meritorious Service Award (1998), IEEE Signal Processing Society Best Paper Award (1998), a IEEE Third Millenium Medal and a 2002 IEEE Signal Processing Society Distinguished Lecturership. He was President of the IEEE Signal Processing Society (2006–2007) and during his term served on the TAB Periodicals Committee (2006). He was a member of the IEEE TAB Society Review Committee (2008) and is Director-elect of IEEE for Division IX (2009). 相似文献
44.
针对目标检测与跟踪时辐射控制问题,提出一种面向协同检测与跟踪的多传感器长时调度方法.首先建立基于部分马尔可夫决策过程(POMDP)的目标跟踪与辐射控制模型;然后以随机分布粒子计算新生目标检测概率,以后验克拉美-罗下界(PCRLB)预测长时跟踪精度,以隐马尔可夫模型(HMM)滤波器推导长时辐射代价;最后构建新生目标检测概率和已有目标跟踪精度约束下辐射控制的长时优化函数,给出基于贪婪搜索的分支定界算法求解最优调度序列.仿真结果验证了所提出方法的有效性. 相似文献
45.
为提高室内动态环境下服务机器人对行人的自然避让能力,对人的运动轨迹模式进行建模,在此基础上引入了将行人运动长、短期预测结合起来的方法．为适应传感器噪声及网络延迟等因素所造成的感知—控制回路中的多源不确定性,将人与机器人的相对位置关系建模为部分可观的马尔可夫状态．采用部分可观的马尔可夫决策过程（POMDP）进行多源不确定性下的概率决策,协调控制机器人全局路径规划、反应式运动及速度控制等行为模块．实验结果验证,它能够实现提前避碰的安全导航,因避免反复的曲折与徘徊运动而提高了机器人导航效率． 相似文献
46.
针对无人机在路径规划过程中会遇到静态或者动态的障碍物,从而导致路径规划失败的问题,提出一种基于部分可观测马尔可夫决策过程（partially observable markov decision process,POMDP）模型的人工势场（artificial potential field,APF）无人机路径规划策略（POMDP-APF）。首先使用传感器获得的障碍物信息结合POMDP模型预测障碍物的未来位置,为无人机的路径规划做准备;其次,提出一种新的基于障碍物的正方体外接球的模型,保障无人机在路径规划过程中的安全性;最后,结合改进的APF算法实现无人机的路径规划。仿真结果表明,POMDP-APF策略在无人机实时路径规划中具有良好的可行性和有效性,使无人机能够有效避开障碍物,同时路径长度以及耗费时间更短。 相似文献
47.
48.
49.
50.