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Integration of General Bayesian Network and ubiquitous decision support to provide context prediction capability

机译:集成通用贝叶斯网络和无处不在的决策支持以提供上下文预测功能

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摘要

We propose a new type of ubiquitous decision support system which is powered by a General Bayesian Network (GBN). While complicated decision support problems are plagued by complexities involved in interpreting causal relationships among decision variables, GBN has shown excellent decision support competence due to its flexible structure which allows itself to extract appropriate and robust causal relationships among target variable and related explanatory variables. The potentials of GBN, however, were not explored enough in the field of ubiquitous decision support area. Hence, we propose a new type of ubiquitous decision support mechanism named U-BASE which uses GBN for context prediction to improve decision support. To prove the validity of the proposed decision support mechanism, we collected a set of contextual data from college students, and applied U-BASE to induce useful and robust results. Practical implications are fully discussed for motivating future studies.
机译:我们提出了一种由通用贝叶斯网络(GBN)支持的新型无处不在的决策支持系统。尽管复杂的决策支持问题因解释决策变量之间的因果关系所涉及的复杂性而困扰,但GBN表现出出色的决策支持能力,这是因为其结构灵活,可以提取目标变量与相关解释变量之间的适当而稳固的因果关系。然而,在无处不在的决策支持领域,GBN的潜力还没有得到充分的挖掘。因此,我们提出了一种新型的普遍存在的决策支持机制,称为U-BASE,它使用GBN进行上下文预测以改善决策支持。为了证明所提出的决策支持机制的有效性,我们从大学生那里收集了一组上下文数据,并应用U-BASE得出了有用且可靠的结果。实际讨论了如何激发未来研究的意义。

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