Extended knowledge attributed Petri Nets

IF 0.9 Q3 COMPUTER SCIENCE, THEORY & METHODS
Attila Fűr
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引用次数: 0

Abstract

Choosing the best way for describing physical reality has always been standing in focus of research. Several methodologies have been developed based on classical mathematics, or statistics and also new disciplines — such as soft-computing techniques — appeared. Petri Nets as one of the most naturalistic modeling methodologies are well suited to describe complex process in general. However in some fields of modeling the describing power of basic Petri Nets proved not to be robust enough, therefore several extensions were made to the original concept. Colored tokens (Colored Petri Nets), stochastic delayed streaming of mobile entities (Stochastic Petri Nets), object oriented architecture (Object Oriented Petri Nets), numerical (Numerical Petri Nets) and linguistic attributes (Fuzzy Petri Nets) broaden the range of capabilities. In some fields of problem solving, usage of static and mobile knowledge bases is needed: e.g., flexible manufacturing systems, or intelligent traffic simulation. These problems to be investigated involved new conceptual developments of Petri Nets and led to the introduction of Knowledge Attributed Petri Nets. At the same time distributed control in simulation appeared, intelligent agents supported the connection of mobile knowledge bases and static inference engines in an effective way. The mentioned extensions brought general support in model synthesis, but some unsolved questions remained related to the implementation of intelligent mobile entities. This paper highlights a new level of AI controlled simulation introducing the Extended Knowledge Attributed Petri Nets that offer the capability of easy implementation of mobile inference engines and knowledge base, providing general mobile AI in Petri Nets.
扩展知识属性Petri网
选择描述物理现实的最佳方式一直是研究的焦点。在经典数学或统计学的基础上开发了几种方法,同时也出现了新的学科,如软计算技术。Petri网作为最自然的建模方法之一,非常适合于描述复杂过程。然而,在一些建模领域,基本Petri网的描述能力被证明不够强大,因此对原始概念进行了一些扩展。彩色标记(彩色Petri网),移动实体的随机延迟流(随机Petri网),面向对象的体系结构(面向对象的Petri网),数值(数值Petri网)和语言属性(模糊Petri网)扩大了功能的范围。在解决问题的某些领域,需要使用静态和移动知识库:例如,柔性制造系统或智能交通模拟。这些有待研究的问题涉及到Petri网的新概念发展,并导致了知识属性Petri网的引入。同时仿真中的分布式控制出现,智能代理有效地支持移动知识库与静态推理引擎的连接。上述扩展为模型综合提供了一般支持,但在智能移动实体的实现方面仍存在一些未解决的问题。本文重点介绍了人工智能控制仿真的一个新水平,引入了扩展知识属性Petri网,它提供了易于实现移动推理引擎和知识库的能力,在Petri网中提供了通用的移动人工智能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
2.50
自引率
16.70%
发文量
0
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