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Lei J.,Yunnan Nationalities University | Lei J.,Key Laboratory of Software Engineering of Yunnan Province | Yang Z.-Y.,Yunnan Nationalities University
Applied Intelligence | Year: 2013

This paper proposes a disturbance management methodology for an agent-based holonic manufacturing system by using hybrid control approach, with its application to a mobile manipulator system. Firstly, the architectures based on holarchy and agents are given. Then the framework of hybrid control strategy is outlined and the stability analysis for the closed-loop system is introduced. The major contribution of this work is the exploration for using hybrid control approach into disturbance management mechanism. The switched disturbance detector is presented and the identification algorithm is given. The hybrid controller is designed to reject the disturbance by switching between reactions against diagnosed symptoms. Finally, the case study onto the mobile manipulator hybrid system verifies the effectiveness and applicability of this design method. It validates the agility, efficiency, and retaining stability to system of the holonic multiagent concept for industrial or other application systems. © 2012 Springer Science+Business Media, LLC.


Fang Q.,Yunnan University | Yue K.,Yunnan University | Yue K.,Key Laboratory of Software Engineering of Yunnan Province | Fu X.,Kunming University of Science and Technology | And 2 more authors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2013

Bayesian network (BN) is the popular and important probabilistic graphical model for representing and inferring uncertain knowledge. Learning BN from massive data is the basis for uncertain-knowledge-centered inferences, prediction and decision. The inherence of massive data makes BN learning be adjusted to the large data volume and executed in parallel. In this paper, we proposed a MapReduce-based approach for learning BN from massive data by extending the traditional scoring & search algorithm. First, in the scoring process, we developed map and reduce algorithms for obtaining the required parameters in parallel. Second, in the search process, for each node we developed map and reduce algorithms for scoring all the candidate local structures in parallel and selecting the local optimal structure with the highest score. Thus, the local optimal structures of each node are merged to the global optimal one. Experimental result indicates our proposed method is effective and efficient. © 2013 Springer-Verlag.


Yue K.,Yunnan University | Yue K.,Key Laboratory of Software Engineering of Yunnan Province | Qian W.,Yunnan University | Fu X.,Kunming University of Science and Technology | And 2 more authors.
Soft Computing | Year: 2015

In time-series environments, uncertain knowledge among variables in a time slice can be represented and modeled by a Bayesian network (BN). In this paper, we are to achieve the global uncertain knowledge during a period of time for decision-making or action selection by fussing or combining the participating uncertainties of multiple time slices consistently while satisfying the demands of high efficiency and instantaneousness. We adopt qualitative probabilistic network (QPN), the qualitative abstraction of BN, as the underlying framework of modeling and fusing time-series uncertain knowledge. The BNs in continuous time slices constitute time-series BNs, from which we derive time-series QPNs. Taking time-series BNs as input, we propose a QPN-based approach to fuse time-series uncertainties in line with temporal specialties. First, for each time slice, we enhance the implied QPN by augmenting interval-valued weights derived from the corresponding BN, and then obtain the QPN with weighted influences, denoted EQPN (Enhanced Qualitative Probabilistic Network), which provides a quantitative and conflict-free basis for fusing uncertain knowledge. Then, we give the method for fusing the graphical structures of time-series EQPNs based on the concept of Markov equivalence. Following, we give a superposition method for fusing qualitative influences of time-series EQPNs. Experimental results show that our method is not only efficient, but also effective. Meanwhile, the simulation results when applying time-series EQPNs and the fusion algorithm to a robotic system show that our method is applicable in realistic intelligent situations. © 2014, Springer-Verlag Berlin Heidelberg.


Xu W.,Yunnan University | Yue K.,Yunnan University | Yue K.,Key Laboratory of Software Engineering of Yunnan Province | Li J.,Key Laboratory of Software Engineering of Yunnan Province | And 4 more authors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2013

Sponsored search auctions play a crucial role in the Internet advertising. By considering the mutual interactions among advertisers in sponsored search auctions, we propose a game-theory based method for advertisers cooperating with each other in a sponsored search auction. First, we propose a cooperation bid strategy for advertisers' coalition, which could make the utility of the coalition increased and be obtained in linear time. Then, we prove the coalitional game of advertisers has a non-empty core containing the Shapley value. Following, we use an approximate Shapley value to distribute the coalition's utility among advertisers in the coalition. Experiments results verify the efficiency and effectiveness of our method. © 2013 Springer-Verlag.


Fang Z.,Yunnan University | Yue K.,Yunnan University | Yue K.,Key Laboratory of Software Engineering of Yunnan Province | Zhang J.,Yunnan University | And 3 more authors.
Mathematical Problems in Engineering | Year: 2015

Most classical search engines choose and rank advertisements (ads) based on their click-through rates (CTRs). To predict an ad's CTR, historical click information is frequently concerned. To accurately predict the CTR of the new ads is challenging and critical for real world applications, since we do not have plentiful historical data about these ads. Adopting Bayesian network (BN) as the effective framework for representing and inferring dependencies and uncertainties among variables, in this paper, we establish a BN-based model to predict the CTRs of new ads. First, we built a Bayesian network of the keywords that are used to describe the ads in a certain domain, called keyword BN and abbreviated as KBN. Second, we proposed an algorithm for approximate inferences of the KBN to find similar keywords with those that describe the new ads. Finally based on the similar keywords, we obtain the similar ads and then calculate the CTR of the new ad by using the CTRs of the ads that are similar with the new ad. Experimental results show the efficiency and accuracy of our method. © 2014 Zhipeng Fang et al.

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