The Experts below are selected from a list of 116643 Experts worldwide ranked by ideXlab platform
Rahul Jain - One of the best experts on this subject based on the ideXlab platform.
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Approachability in Stackelberg Stochastic Games with Vector Costs
Dynamic Games and Applications, 2017Co-Authors: Dileep Kalathil, Vivek S. Borkar, Rahul JainAbstract:The notion of approachability was introduced by Blackwell (Pac J Math 6(1):1–8, 1956 ) in the context of vector-valued repeated games. The famous ‘Blackwell’s approachability theorem’ prescribes a strategy for approachability, i.e., for ‘steering’ the average vector cost of a given agent toward a given target set, irrespective of the strategies of the other agents. In this paper, motivated by the multi-objective Optimization/Decision-making problems in dynamically changing environments, we address the approachability problem in Stackelberg stochastic games with vector-valued cost functions. We make two main contributions. Firstly, we give a simple and computationally tractable strategy for approachability for Stackelberg stochastic games along the lines of Blackwell’s. Secondly, we give a reinforcement learning algorithm for learning the approachable strategy when the transition kernel is unknown. We also recover as a by-product Blackwell’s necessary and sufficient conditions for approachability for convex sets in this setup and thus a complete characterization. We give sufficient conditions for non-convex sets.
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approachability in stackelberg stochastic games with vector costs
arXiv: Learning, 2014Co-Authors: Dileep Kalathil, Vivek S. Borkar, Rahul JainAbstract:The notion of approachability was introduced by Blackwell [1] in the context of vector-valued repeated games. The famous Blackwell's approachability theorem prescribes a strategy for approachability, i.e., for `steering' the average cost of a given agent towards a given target set, irrespective of the strategies of the other agents. In this paper, motivated by the multi-objective Optimization/Decision making problems in dynamically changing environments, we address the approachability problem in Stackelberg stochastic games with vector valued cost functions. We make two main contributions. Firstly, we give a simple and computationally tractable strategy for approachability for Stackelberg stochastic games along the lines of Blackwell's. Secondly, we give a reinforcement learning algorithm for learning the approachable strategy when the transition kernel is unknown. We also recover as a by-product Blackwell's necessary and sufficient condition for approachability for convex sets in this set up and thus a complete characterization. We also give sufficient conditions for non-convex sets.
Cheng Qian - One of the best experts on this subject based on the ideXlab platform.
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game theory based real time shop floor scheduling strategy and method for cloud manufacturing
International Journal of Intelligent Systems, 2017Co-Authors: Yingfeng Zhang, Jin Wang, Sichao Liu, Cheng QianAbstract:With the rapid advancement and widespread application of information and sensor technologies in manufacturing shop floor, the typical challenges that cloud manufacturing is facing are the lack of real-time, accurate, and value-added manufacturing information, the efficient shop floor scheduling strategy, and the method based on the real-time data. To achieve the real-time data-driven Optimization Decision, a dynamic Optimization model for flexible job shop scheduling based on game theory is put forward to provide a new real-time scheduling strategy and method. Contrast to the traditional scheduling strategy, each machine is an active entity that will request the processing tasks. Then, the processing tasks will be assigned to the optimal machines according to their real-time status by using game theory. The key technologies such as game theory mathematical model construction, Nash equilibrium solution, and Optimization strategy for process tasks are designed and developed to implement the dynamic Optimization model. A case study is presented to demonstrate the efficiency of the proposed strategy and method, and real-time scheduling for four kinds of exceptions is also discussed.
Dileep Kalathil - One of the best experts on this subject based on the ideXlab platform.
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Approachability in Stackelberg Stochastic Games with Vector Costs
Dynamic Games and Applications, 2017Co-Authors: Dileep Kalathil, Vivek S. Borkar, Rahul JainAbstract:The notion of approachability was introduced by Blackwell (Pac J Math 6(1):1–8, 1956 ) in the context of vector-valued repeated games. The famous ‘Blackwell’s approachability theorem’ prescribes a strategy for approachability, i.e., for ‘steering’ the average vector cost of a given agent toward a given target set, irrespective of the strategies of the other agents. In this paper, motivated by the multi-objective Optimization/Decision-making problems in dynamically changing environments, we address the approachability problem in Stackelberg stochastic games with vector-valued cost functions. We make two main contributions. Firstly, we give a simple and computationally tractable strategy for approachability for Stackelberg stochastic games along the lines of Blackwell’s. Secondly, we give a reinforcement learning algorithm for learning the approachable strategy when the transition kernel is unknown. We also recover as a by-product Blackwell’s necessary and sufficient conditions for approachability for convex sets in this setup and thus a complete characterization. We give sufficient conditions for non-convex sets.
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approachability in stackelberg stochastic games with vector costs
arXiv: Learning, 2014Co-Authors: Dileep Kalathil, Vivek S. Borkar, Rahul JainAbstract:The notion of approachability was introduced by Blackwell [1] in the context of vector-valued repeated games. The famous Blackwell's approachability theorem prescribes a strategy for approachability, i.e., for `steering' the average cost of a given agent towards a given target set, irrespective of the strategies of the other agents. In this paper, motivated by the multi-objective Optimization/Decision making problems in dynamically changing environments, we address the approachability problem in Stackelberg stochastic games with vector valued cost functions. We make two main contributions. Firstly, we give a simple and computationally tractable strategy for approachability for Stackelberg stochastic games along the lines of Blackwell's. Secondly, we give a reinforcement learning algorithm for learning the approachable strategy when the transition kernel is unknown. We also recover as a by-product Blackwell's necessary and sufficient condition for approachability for convex sets in this set up and thus a complete characterization. We also give sufficient conditions for non-convex sets.
Hasna Louahlia-gualous - One of the best experts on this subject based on the ideXlab platform.
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Review of tri-generation technologies: Design evaluation, Optimization, Decision-making, and selection approach
Energy Conversion and Management, 2016Co-Authors: Houssein Al Moussawi, Hasna Louahlia-gualousAbstract:Electricity, heating, and cooling are the three main components constituting the tripod of energy consumption in residential, commercial, and public buildings all around the world. Their separate generation causes higher fuel consumption, at a time where energy demands and fuel costs are continuously rising. Combined cooling, heating, and power (CCHP) or trigeneration could be a solution for such challenge yielding an efficient, reliable, flexible, competitive, and less pollutant alternative. A variety of trigeneration technologies are available and their proper choice is influenced by the employed energy system conditions and preferences. In this paper, different types of trigeneration systems are classified according to the prime mover, size and energy sequence usage. A leveled selection procedure is subsequently listed in the consecutive sections. The first level contains the applied prime mover technologies which are considered to be the heart of any CCHP system. The second level comprises the heat recovery equipment (heating and cooling) of which suitable selection should be compatible with the used prime mover. The third level includes the thermal energy storage system and heat transfer fluid to be employed. For each section of the paper, a survey of conducted studies with CHP/CCHP implementation is presented. A comprehensive table of evaluation criteria for such systems based on energy, exergy, economy, and environment measures is performed, along with a survey of the methods used in their design, Optimization, and Decision-making. Moreover, a classification diagram of the main CHP/CCHP system components is summarized. A general selection approach of the appropriate CCHP system according to specific needs is finally suggested. In almost all reviewed works, CCHP systems are found to have positive technical and performance impacts.
Fushen Xue - One of the best experts on this subject based on the ideXlab platform.
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Electricity Purchase Optimization Decision Based on Data Mining and Bayesian Game
Energies, 2018Co-Authors: Yajing Gao, Xiaojie Zhou, Jiafeng Ren, Zheng Zhao, Fushen XueAbstract:The openness of the electricity retail market results in the power retailers facing fierce competition in the market. This article aims to analyze the electricity purchase Optimization Decision-making of each power retailer with the background of the big data era. First, in order to guide the power retailer to make a purchase of electricity, this paper considers the users’ historical electricity consumption data and a comprehensive consideration of multiple factors, then uses the wavelet neural network (WNN) model based on “meteorological similarity day (MSD)” to forecast the user load demand. Second, in order to guide the quotation of the power retailer, this paper considers the multiple factors affecting the electricity price to cluster the sample set, and establishes a Genetic algorithm- back propagation (GA-BP) neural network model based on fuzzy clustering (FC) to predict the short-term market clearing price (MCP). Thirdly, based on Sealed-bid Auction (SA) in game theory, a Bayesian Game Model (BGM) of the power retailer’s bidding strategy is constructed, and the optimal bidding strategy is obtained by obtaining the Bayesian Nash Equilibrium (BNE) under different probability distributions. Finally, a practical example is proposed to prove that the model and method can provide an effective reference for the Decision-making Optimization of the sales company.
