A Petri net is called fork-attribution if it is choice-free (a unique output transition for each place) and join-free (a unique input place for each transition). Synthesis tries, for a given (finite) labelled transition system (LTS), to find an (unlabelled) Petri net with an isomorphic reachability graph. Synthesis often requires a large set of inequality systems to be solved, making it quite costly. In presynthesis we exploit common necessary properties of some class of Petri nets. If any of the properties do not hold, we can directly dismiss the LTS, it cannot be the reachability graph of a Petri net from our class. This also allows an implementation to give a meaningful response instead of just telling the user that some inequality system is unsolvable. If all properties hold, we may gain additional information that can simplify the inequality systems to be solved. In this paper, we enhance theoretically, and tune algorithmically a presynthesis strategy already known for choice-free nets to fork-attribution nets.

如果无选择（每个位置的唯一输出过渡）和无连接（每个过渡的唯一输入处）的Petri网称为分叉属性。对于给定的（有限的）标记过渡系统（LTS），合成尝试找到具有同构可达性图的（未标记的）Petri网。综合通常需要解决大量不平等系统，因此成本很高。在预合成中，我们利用了某些类Petri网的常见必要属性。如果任何一个属性都不成立，我们可以直接取消LTS，它不能是我们班级中Petri网的可达性图。这也使实现能够给出有意义的响应，而不仅仅是告诉用户某些不平等系统是无法解决的。如果所有属性都成立，我们可能会获得其他信息，可以简化要解决的不平等系统。在本文中，我们从理论上进行了增强，并在算法上调整了一种已知的预合成策略，该策略已将无选择网络称为分叉属性网络。