Anthony Spiteri Staines



An Introduction to Bi-Directional Transition Network Modeling

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Ordinary Petri nets are classifiable as forward transition systems. This implies that by default, once a transition has fired it cannot be reversed. System representation and modeling is an interesting area used for modeling modern computer systems. Because of changes in technology systems contain diverse forms of behavior. In requirements engineering and requirements elicitation, system modelers can benefit from new approaches and new forms of system representation that build upon previous work. This work presents a new approach and view where it is possible to actually reverse a transition that has taken place. This idea is presented in a Bi-Directional or Bi-Directed transition net. The bi-directional net introduces the concept of reversing the normal firing order. In normal Petri nets once the firing occurs this is irreversible. In the bi-directional net it is possible to reverse the transition. In this work the motivation and ideas behind the Bi-Directional transition are explained and compared with normal Petri Nets. Some toy examples are included to support the ideas of the bi-directional net. From the findings and examples it is clearly indicated that the Bi-Directional Net can be used to create models that can invert the transition firing order and reverse their behavior. Even though the Bi-Directional Net might look simpler in reality it is more complex. The results discuss some of the main properties and issues behind the bi-directional net. This paper is divided into the following sections: i) introduction to the area, ii) related work about transition systems and Petri Nets, iii) motivation and problem definition, iv) proposed solution, v) implementation, vi) examples, vii) results and findings and viii) conclusions.


Bi-directionality, Symbolic Representation, Systems Modeling, Network Representation, Petri Nets, Transition Systems


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Cite this paper

Anthony Spiteri Staines. (2017) An Introduction to Bi-Directional Transition Network Modeling. International Journal of Computers, 2, 80-87


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