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Optimal control of unknown nonaffine nonlinear discrete-time systems based on adaptive dynamic programming

AUTOMATICA 2012年第8期48卷 1825-1832页

作者： Wang, Ding Liu, Derong Wei, Qinglai Zhao, Dongbin Jin, Ning Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Illinois Dept Elect & Comp Engn Chicago IL 60607 USA

An intelligent-optimal control scheme for unknown nonaffine nonlinear discrete-time systems with discount factor in the cost function is developed in this paper. The iterative adaptive dynamic programming algorithm is introduced to solve the optimal control problem with convergence analysis. Then, the implementation of the iterative algorithm via globalized dual heuristic programming technique is presented by using three neural networks, which will approximate at each iteration the cost function, the control law, and the unknown nonlinear system, respectively. In addition, two simulation examples are provided to verify the effectiveness of the developed optimal control approach. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Adaptive critic designs Adaptive dynamic programming Approximate dynamic programming Globalized dual heuristic programming Intelligent control Neural network Optimal control

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Pure-Strategy Nash Equilibria of GSP keyword Auction

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JOURNAL OF THE ASSOCIATION FOR INFORMATION systems 2012年第2期13卷 57-87页

作者： Li, Linjing Zeng, Daniel Zhao, Huimin Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing Peoples R China Univ Wisconsin Milwaukee Sheldon B Lubar Sch Business Milwaukee WI USA

Despite the tremendous commercial success of generalized second-price (GSP) keyword auctions, it still remains a big challenge for an advertiser to formulate an effective bidding strategy. In this paper, we strive to bridge this gap by proposing a framework for studying pure-strategy Nash equilibria in GSP auctions. We first analyze the equilibrium bidding behaviors by investigating the properties and distribution of all pure-strategy Nash equilibria. Our analysis shows that the set of all pure-strategy Nash equilibria of a GSP auction can be partitioned into separate convex polyhedra based on the order of bids if the valuations of all advertisers are distinct. We further show that only the polyhedron that allocates slots efficiently is weakly stable, thus allowing all inefficient equilibria to be weeded out. We then propose a novel refinement method for identifying a set of equilibria named the stable Nash equilibrium set (STNE) and prove that STNE is either the same as or a proper subset of the set of the well-known symmetrical Nash equilibria. These findings free both auctioneers and advertisers from complicated strategic thinking. The revenue of a GSP auction on STNE is at least the same as that of the classical Vickrey-Clarke-Groves mechanism and can be used as a benchmark for evaluating other mechanisms. At the same time, STNE provides advertisers a simple yet effective and stable bidding strategy.

关键词： keyword Advertising Stability Generalized Second-Price Sponsored Search Nash Equilibrium

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Neural-Network-Based Optimal control for a Class of Unknown Discrete-Time Nonlinear systems Using Globalized Dual Heuristic Programming

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2012年第3期9卷 628-634页

作者： Liu, Derong Wang, Ding Zhao, Dongbin Wei, Qinglai Jin, Ning Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Illinois Dept Elect & Comp Engn Chicago IL 60607 USA

In this paper, a neuro-optimal control scheme for a class of unknown discrete-time nonlinear systems with discount factor in the cost function is developed. The iterative adaptive dynamic programming algorithm using globalized dual heuristic programming technique is introduced to obtain the optimal controller with convergence analysis in terms of cost function and control law. In order to carry out the iterative algorithm, a neural network is constructed first to identify the unknown controlled system. Then, based on the learned system model, two other neural networks are employed as parametric structures to facilitate the implementation of the iterative algorithm, which aims at approximating at each iteration the cost function and its derivatives and the control law, respectively. Finally, a simulation example is provided to verify the effectiveness of the proposed optimal control approach.

关键词： Adaptive dynamic programming approximate dynamic programming globalized dual heuristic programming intelligent control neural networks optimal control

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Click frauds and price determination models

Click frauds and price determination models

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2012 10th IEEE International Conference on Intelligence and Security Informatics, ISI 2012

作者： Li, Xiarong Zeng, Daniel Wang, Lei State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences China University of Arizona United States

ISBN: (纸本)9781467321037

Click fraud (CF) has become a serious problem in the online advertising, making the anti-CF issue quite important. In this paper, we analyze the effects of the price determination model on the CF situations in online advertising. Our theoretical results show that the flat-rate model can induce more click frauds than the real-time-bidding model. Our finding is validated with a real advertising dataset. © 2012 IEEE.

关键词： Marketing

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Listwise approaches based on feature ranking discovery

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Frontiers of Computer Science 2012年第6期6卷 647-659页

作者： Yongqing WANG Wenji MAO Daniel ZENG Fen XIA Commercial Products Development Department Alibaba Inc.Hangzhou 310052China State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Department of Management Information Systems University of ArizonaTucsonAZ 85721USA Union Research and Development Department Baidu Inc.Beijing 100085China

Listwise approaches are an important class of learning to rank, which utilizes automatic learning techniques to discover useful information. Most previous research on listwise approaches has focused on optimizing ranking models using weights and has used imprecisely labeled training data; optimizing ranking models using features was largely ignored thus the continuous performance improvement of these approaches was hindered. To address the limitations of previous listwise work, we propose a quasi-KNN model to discover the ranking of features and employ rank addition rule to calculate the weight of combination. On the basis of this, we propose three listwise algorithms, FeatureRank, BL-FeatureRank, and DiffRank. The experimental results show that our proposed algorithms can be applied to a strict ordered ranking training set and gain better performance than state-of-the-art listwise algorithms.

关键词： learning to rank listwise approach feature＇s ranking discovery

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Parallel Traffic management System Helps 16th Asian Games

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IEEE INTELLIGENT systems 2012年第3期27卷 74-78页

作者： Xiong, Gang Liu, Sheng Dong, Xisong Zhu, Fenghua Hu, Bin Fan, Dong Zhang, Zi Chinese Acad Sci Beijing Engn Res Ctr Intelligent Technol & Syst State Key Lab Management & Control Complex Syst Beijing 100864 Peoples R China Chinese Acad Sci Cloud Comp Ind Technol Innovat & Incubat Ctr Dongguan Res Inst Beijing 100864 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100864 Peoples R China

To overcome public transportation problems during the 16th Asian Games held in Guanhzhou China, a PtMS (Parallel Transportation management System), a novel application of Intelligent Transportation systems, was introduced for effective and convenient traffic management. Results show that PtMS has successfully enhanced public traffic management, raising it from experience-based policy formulation plus manual implementation to scientific computing-based policy generation plus implementation with intelligent systems.

关键词： Parallel Transportation management System Public Transit Asian Games Bus Rapid Transit Bus management Taxi management Artificial Transportation System

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Modeling Social Causality and Responsibility Judgment in Multi-Agent Interactions

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JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH 2012年第1期44卷 223-273页

作者： Mao, Wenji Gratch, Jonathan Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China Univ So Calif Inst Creat Technol Playa Vista CA 90094 USA

Social causality is the inference an entity makes about the social behavior of other entities and self. Besides physical cause and effect, social causality involves reasoning about epistemic states of agents and coercive circumstances. Based on such inference, responsibility judgment is the process whereby one singles out individuals to assign responsibility, credit or blame for multi-agent activities. Social causality and responsibility judgment are a key aspect of social intelligence, and a model for them facilitates the design and development of a variety of multi-agent interactive systems. Based on psychological attribution theory, this paper presents a domain-independent computational model to automate social inference and judgment process according to an agent's causal knowledge and observations of interaction. We conduct experimental studies to empirically validate the computational model. The experimental results show that our model predicts human judgments of social attributions and makes inferences consistent with what most people do in their judgments. Therefore, the proposed model can be generically incorporated into an intelligent system to augment its social and cognitive functionality.

关键词： Blame Agents social model Judgment Social Intelligence Intelligent systems Attribution Theory Reasoning

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Extracting action knowledge in security informatics

Extracting action knowledge in security informatics

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2012 10th IEEE International Conference on Intelligence and Security Informatics, ISI 2012

作者： Ge, Ansheng Mao, Wenji Zeng, Daniel Kong, Qingchao Zhu, Huachi State Key Laboratory of Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China Beijing Police Department China

ISBN: (纸本)9781467321037

Actions are the primary way an entity interacts with other entities and acts on the external world. Action knowledge is of vital importance for behavior modeling, analysis and prediction in security informatics. In this paper, we present our approach to action knowledge extraction from Web textual data. Our approach is based on mutual bootstrapping with knowledge reasoning, which can acquire more action knowledge types and require less human participation compared with the related work. We evaluate the performance of our method and demonstrate its effectiveness through experiment. © 2012 IEEE.

关键词： Informatics Action Epistemology role model Information Extraction Security

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The research on micro-blog public opinion index and the application of prototype system

The research on micro-blog public opinion index and the appl...

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2012 9th IEEE International Conference on Networking, Sensing and control, ICNSC 2012

作者： Lu, Hao Yang, Yanwu Gan, Runsheng Zhang, Nan State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Chinese Association of Automation Beijing China

ISBN: (纸本)9781467303880

The micro-blogs, as a new social media, possesses big differences with other social media on the aspect of information updating frequency, organization structure, user connection and etc, which have astonishing power of convergence and penetration. Based on this, it is proposed in this paper that Micro-Blog Public Opinion Index (MBPOI), which consists of five sub-indexes QI, II, RI, PI and CI (Quantity Index, Intensity Index, Relation Index, Polarity Index, Confidence Index) multi-dimensionally, is used to measure and evaluate the public topics and issues discussed in the micro-blogs. In the meanwhile, taking "ABB automatic world 2011" activity as example, the MBPOI prototype system is verified;it has been shown from the results that the MBPOI, which uses five sub-indexes method, owns much better effect by quantifying the topics/issues' influence in multi-dimensions and multi-levels, and provides effective micro-blogs analysis reports for monitoring and tracking the "ABB automatic world 2011" activity. © 2012 IEEE.

关键词： Social networking (online)

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Budget planning for coupled campaigns in search auctions

Budget planning for coupled campaigns in search auctions

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22nd Workshop on Information Technologies and systems, WITS 2012

作者： Yang, Yanwu Qin, Rui Zhang, Jie Li, Xin Zeng, Daniel State Key Lab of Intelligent Control and Management of Complex Systems Inst. of Automation CAS Beijing China Department of Information Systems City University of Hong Kong Kowloon Tong Hong Kong Department of Management Information Systems University of Arizona United States

Budget-related decisions in search auctions are recognized as a structured decision problem, rather than a simple constraint. Budget planning over several coupled campaigns remains a challenging but utterly important task in search advertisements. In this paper, we propose a multi-campaign budget plan- ning approach using optimal control techniques, with consideration of the substitute relationship between advertising campaigns. A measure of coupled relationships between campaigns is presented, e.g., the over- lapping degree (O) in terms of campaign contents, promotional periods and target regions. We also discuss some desirable properties of our model and possible solutions. Furthermore, computational experiments are conducted to evaluate our model and identified properties, with real-world data collected from logs and reports of practical campaigns. Experimental results show that, (a) coupled campaigns with higher over- lapping degrees can reduce the optimal budget level and the optimal revenue, and also arrive the budgeting cap earlier;(b) The advertising effort could be seriously weakened when ignoring the overlapping degree between campaigns.

关键词： Budget control

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