We propose several heuristics for mitigating one of the main causes of combinatorial explosion in rank-based complementation of B\"{u}chi automata (BAs): unnecessarily high bounds on the ranks of states. First, we identify elevator automata, which is a large class of BAs (generalizing semi-deterministic BAs), occurring often in practice, where ranks of states are bounded according to the structure of strongly connected components. The bounds for elevator automata also carry over to general BAs that contain elevator automata as a sub-structure. Second, we introduce two techniques for refining bounds on the ranks of BA states using data-flow analysis of the automaton. We implement out techniques as an extension of the tool Ranker for BA complementation and show that they indeed greatly prune the generated state space, obtaining significantly better results and outperforming other state-of-the-art tools on a large set of benchmarks.
翻译:我们建议用几种理论来减轻B\"{u}chi automata(BAs)按级补充的组合爆炸的主要原因之一:不必要地在各州的等级上设置很高的界限。 首先,我们确定电梯自动模型,这是一大批BA(一般为半确定性BA),通常在实际中发生,国家等级根据紧密相连的组件结构进行约束。电梯自动模型的界限也转移到一般BA,其中含有电梯自动数据,作为子结构。第二,我们采用两种技术,利用对Automaton的数据流分析来改进BA国家等级的界限。我们采用外部技术作为BA补充工具序列的延伸,并表明它们确实大大地利用了生成的州空间,取得了显著更好的结果,并在大量基准上优异于其他最先进的工具。