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Fuzzy Stochastic Timed Petri Nets for Causal properties representation
arXiv - CS - Computation and Language Pub Date : 2020-11-24 , DOI: arxiv-2011.12075
Alejandro Sobrino, Eduardo C. Garrido-Merchan, Cristina Puente

Imagery is frequently used to model, represent and communicate knowledge. In particular, graphs are one of the most powerful tools, being able to represent relations between objects. Causal relations are frequently represented by directed graphs, with nodes denoting causes and links denoting causal influence. A causal graph is a skeletal picture, showing causal associations and impact between entities. Common methods used for graphically representing causal scenarios are neurons, truth tables, causal Bayesian networks, cognitive maps and Petri Nets. Causality is often defined in terms of precedence (the cause precedes the effect), concurrency (often, an effect is provoked simultaneously by two or more causes), circularity (a cause provokes the effect and the effect reinforces the cause) and imprecision (the presence of the cause favors the effect, but not necessarily causes it). We will show that, even though the traditional graphical models are able to represent separately some of the properties aforementioned, they fail trying to illustrate indistinctly all of them. To approach that gap, we will introduce Fuzzy Stochastic Timed Petri Nets as a graphical tool able to represent time, co-occurrence, looping and imprecision in causal flow.

中文翻译:

因果属性表示的模糊随机定时Petri网

图像通常用于建模,表示和交流知识。特别地,图形是最强大的工具之一,能够表示对象之间的关系。因果关系通常由有向图表示,节点表示原因,链接表示因果影响。因果图是骨骼图片,显示因果关联和实体之间的影响。用于以图形表示因果情景的常用方法是神经元,真值表,因果贝叶斯网络,认知图和Petri网。因果关系通常按优先级(原因在结果之前),并发(并发(通常由两个或多个原因同时引起)),循环性(原因引起效果,而结果加强原因)和不精确(原因的存在有利于效果,但不一定会导致它)。我们将证明,即使传统的图形模型能够分别表示上述某些属性,它们也无法尝试模糊地说明所有这些属性。为了弥补这一差距,我们将引入模糊随机定时Petri网作为一种图形工具,能够表示因果流中的时间,共现,循环和不精确性。
更新日期:2020-11-25
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