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1.
BDI模型能够很好地解决在特定环境下的Agent的推理和决策问题,但在动态和不确定环境下缺少决策和学习的能力。强化学习解决了Agent在未知环境下的决策问题,却缺少BDI模型中的规则描述和逻辑推理。针对BDI在未知和动态环境下的策略规划问题,提出基于强化学习Q-learning算法来实现BDI Agent学习和规划的方法,并针对BDI的实现模型ASL的决策机制做出了改进,最后在ASL的仿真平台Jason上建立了迷宫的仿真,仿真实验表明,在加入Q-learning学习机制后的新的ASL系统中,Agent在不确定环境下依然可以完成任务。 相似文献
2.
Ye Wan Jun Tan Guangling Song Chuanwei Yan 《Metallurgical and Materials Transactions A》2006,37(7):2313-2316
The focused ion beam technique was employed to investigate the atmospheric corrosion morphology of AZ91D. It was found that
the α matrix of the alloy was preferentially corroded in the areas adjacent to intermetallic phases. The most interesting
finding was that the corrosion products in corrosion cavities were pelletlike, which has never been reported before. 相似文献
3.
Xue‐Yong Liu Xiao‐Bin Ding Zhao‐Hui Zheng Yu‐Xing Peng Albert S
C Chan C
W Yip Xin‐Ping Long 《Polymer International》2003,52(2):235-240
Amphiphilic magnetic microspheres ranging in diameter from 5 to 100 µm were prepared by dispersion copolymerization of styrene and poly(ethylene oxide) vinylbenzyl (PEO‐VB) macromonomer (MPEO) in the presence of Fe3O4 magnetic fluid. The effects of various polymerization parameters on the average particle size were systematically investigated. The average particle size was found to increase with increasing styrene concentration and initiator concentration. It also increased with decreasing stabilizer concentration and molecular weight of MPEO. The content of the hydroxyl groups localized in the microspheres ranged from 0.01 to 0.2 mmol g?1. © 2003 Society of Chemical Industry 相似文献
4.
Depending on whether bidirectional links or unidirectional links are used for communications, the network topology under a
given range assignment is either an undirected graph referred to as the bidirectional topology, or a directed graph referred
to as the unidirectional topology. The Min-Power Bidirectional (resp., Unidirectional) k-Node Connectivity problem seeks a range assignment of minimum total power subject to the constraint that the produced bidirectional
(resp. unidirectional) topology is k-vertex connected. Similarly, the Min-Power Bidirectional (resp., Unidirectional) k-Edge Connectivity problem seeks a range assignment of minimum total power subject to the constraint the produced bidirectional
(resp., unidirectional) topology is k-edge connected.
The Min-Power Bidirectional Biconnectivity problem and the Min-Power Bidirectional Edge-Biconnectivity problem have been studied
by Lloyd et al. [23]. They show that range assignment based the approximation algorithm of Khuller and Raghavachari [18],
which we refer to as Algorithm KR, has an approximation ratio of at most 2(2 – 2/n)(2 + 1/n) for Min-Power Bidirectional Biconnectivity, and range assignment based on the approximation algorithm of Khuller and Vishkin [19],
which we refer to as Algorithm KV, has an approximation ratio of at most 8(1 – 1/n) for Min-Power Bidirectional Edge-Biconnectivity.
In this paper, we first establish the NP-hardness of Min-Power Bidirectional (Edge-) Biconnectivity. Then we show that Algorithm KR has an approximation ratio of at most 4 for both Min-Power Bidirectional Biconnectivity and Min-Power Unidirectional Biconnectivity,
and Algorithm KV has an approximation ratio of at most 2k for both Min-Power Bidirectional k-Edge Connectivity and Min-Power Unidirectional k-Edge Connectivity. We also propose a new simple constant-approximation algorithm for both Min-Power Bidirectional Biconnectivity
and Min-Power Unidirectional Biconnectivity. This new algorithm applies only to Euclidean instances, but is best suited for
distributed implementation.
A preliminary version of this work appeared in the proceedings of the 2nd International Conference on AD-HOC Network and Wireless
(Adhoc-Now 2003).
Research performed in part while visiting the Max-Plank-Institut fur Informatik.
Gruia Calinescu is an Assistant Professor of Computer Science at the Illinois Institute of Technology since 2000. He held postdoc or visiting
researcher positions at DIMACS, University of Waterloo, and Max-Plank Institut fur Informatik. Gruia has a Diploma from University
of Bucharest and a Ph.D. from Georgia Insitute of Technology. His research interests are in the area of algorithms.
Peng-Jun Wan has joined the Computer Science Department at Illinois Institute of Technology in 1997 and has been an Associate Professor
since 2004. He received his Ph.D. in Computer Science from University of Minnesota in 1997, M.S. in Operations Research and
Control Theory from Chinese Academy of Science in 1993, and B.S. in Applied Mathematics from Tsinghua University in 1990.
His research interests include optical networks and wireless networks. 相似文献
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7.
Bong Keun Kim Wan Kyun Chung 《Industrial Electronics, IEEE Transactions on》2003,50(6):1207-1216
Disturbance-observer (DOB)-based controller design is one of the most popular methods in the field of motion control. In this paper, the generalized disturbance compensation framework, named the robust internal-loop compensator (RIC) is introduced and an advanced design method of a DOB is proposed based on the RIC. The mixed sensitivity optimization problem, which is the main issue of DOB design, is also solved through the parametrization of the DOB in the RIC framework. Differently from conventional methods, the Q-filter is separated from the mixed sensitivity optimization problem and a systematic design law for the DOB is proposed. This guarantees the robustness and optimality of the DOB and enables the design for unstable plants. 相似文献
8.
9.
Six months of data from the YSCAT94 experiment conducted at the CCIW WAVES research platform on Lake Ontario, Canada, are analyzed to evaluate a compound probability model. YSCAT was an ultrawideband small footprint (≈1 m) microwave scatterometer that operated at frequencies of 2-18 GHz, incidence angles from 0° to 60°, both h-pol and v-pol, and which tracked the wind using simultaneous weather measurements. The probability distribution function of the measured instantaneous backscattered amplitude (p(a)) is compared to theoretical distributions developed from-the composite model and a simple wave spectrum. Model parameters of the resulting Rayleigh/generalized lognormal distribution probability density function (pdf) (C, a1 , and a2) are derived directly from the data and are found to demonstrate relationships with wind speed, incidence angle, and radar frequency 相似文献
10.