The information describing the conditions of a system or a person is constantly evolving and may become obsolete and contradict other information. A database, therefore, must be consistently updated upon the acquisition of new valid observations that contradict obsolete ones contained in the database. In this paper, we propose a novel approach for dealing with the information obsolescence problem. Our approach aims to detect, in real-time, contradictions between observations and then identify the obsolete ones, given a representation model. Since we work within an uncertain environment characterized by the lack of information, we choose to use a Bayesian network as our representation model and propose a new approximate concept, $\epsilon$-Contradiction. The new concept is parameterised by a confidence level of having a contradiction in a set of observations. We propose a polynomial-time algorithm for detecting obsolete information. We show that the resulting obsolete information is better represented by an AND-OR tree than a simple set of observations. Finally, we demonstrate the effectiveness of our approach on a real elderly fall-prevention database and showcase how this tree can be used to give reliable recommendations to doctors. Our experiments give systematically and substantially very good results.
翻译:描述一个系统或一个人的条件的信息正在不断演变,可能过时,与其他信息相矛盾。因此,一个数据库必须在获得与数据库中的过时信息相矛盾的新的有效观测结果后不断更新。在本文件中,我们提出了处理信息过时问题的新办法。我们的方法是实时检测各种观测结果之间的矛盾,然后根据一种代表模式确定过时信息。由于我们是在一个以缺乏信息为特征的不确定环境中工作的,我们选择使用一个巴耶斯网络作为我们的代表模式,并提出了一个新的近似概念,即$/epsilon$-Contraction。新概念的参数是信任度,即与一套观测结果相矛盾。我们建议采用多时算法来检测过时信息。我们表明由此产生的过时信息比简单的观察结果更能用一棵和一棵树和一棵树来代表。最后,我们展示了我们如何有效地使用一个真正的老年人预防跌落数据库,并展示如何利用这棵树向医生提供可靠建议。我们的实验有系统和基本良好的结果。