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In this paper, we propose the first deep reinforce-ment learning framework to estimate the optimal Dynamic Treat-ment Regimes from observational medical data. This framework is more flexible and adaptive for high dimensional action and state spaces than existing reinforcement learning methods to model real life complexity in heterogeneous disease progression and treatment choices, with the goal to...
Web entities are the building blocks of human knowledge and users are making decisions among vast varieties of entities. For example, recommendation systems generate lists of entities to users, but seldom show the reasons of recommendation such as the uniqueness of each item to assist user decision making. In this paper, we mathematically define Web entity uniqueness and uniqueness patterns, based...
This paper formulates a schema matching model to analyze the model selection problem arising from pattern recognize and schema matching between infrastructures and different type build-operate-transfer (BOT) project. Through systematical review, an analysis of relevant literature and cases analysis yielded significant factors that would have a certain impact on the feasibility of any BOT project....
With environment worsening and resource decreasing, enterprises have to bear more and more pressures on the environmental protection. How to reduce the due responsibility of enterprises for environmental protection in the life cycle of products has became the focus problem of the decision-makers. The strategy of ecotype supply chain can solve this problem effectively. In this paper, the authors first...
This paper presents a complete multiagent framework for dynamic job shop scheduling, with an emphasis on robustness and adaptability. It provides both a theoretical basis and some experimental justifications for such a framework: a job dispatching procedure for a completely reactive scheduling approach, combining real-time and predictive decision making. It resolves various disruptions as flexibly...
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