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A reusable model of causal graph in qualitative reasoning

机译:定性推理中因果图的可重用模型

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This paper proposes a new data reuse model for graph-based simulation in qualitative reasoning. Qualitative-based simulation is one of important and traditional tools to analyze a dynamic system and to initiate knowledge and principles for students in artificial intelligence. To simply make a model and run a simulation, the graph-based qualitative simulation is a promising method to enhance the efficiency of making graph model, correctness of the model, and understanding the simulated essence. When a simulation model is developed and given the condition/assumption of nodes and arcs in actual use of simulator, the model is completed by referring from our defined set of components. Further, we propose a method to make a graph model based on tree-structured chart and a reuse method of sub-graph in the simulation graph model. The contributions and advantages of this paper can be described as follows. Our proposed making graph method and reuse method can easily make the simulation model. The graph can be also checked whether there are error by oversight or not, since the model can be shown visually on the implemented simulator.
机译:本文提出了一种新的数据重用模型,用于在定性推理中基于图形的模拟。基于定性的模拟是分析动态系统的重要和传统工具之一,并为人工智能学生发起知识和原则。为了简单地制作模型并运行模拟,基于图形的定性仿真是提高制作图形模型,模型正确性的效率的有希望的方法,并理解模拟本质。当开发仿真模型并给出在实际使用中的节点和弧的条件/假设时,通过从我们定义的组件集中引用来完成模型。此外,我们提出了一种基于树结构图的图形模型的方法和模拟图模型中的子图的重用方法。本文的贡献和优点可以描述如下。我们建议的制作图形方法和重用方法可以轻松制作模拟模型。图表也可以检查是否存在误差,因为可以在实现的模拟器上用视觉显示模型。

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