Reduction and Learning Techniques for omega-Automata

这个项目的目的是研究无限单词自动机(omega-Automata)的约简和学习技术。其中一个核心方面是开发用于从有限的例词集合构建omega自动机的算法。对于自动机有限词，这是一个已经被深入研究的课题，但对于无限词，几乎没有结果。此外，我们还希望提出欧米伽自动机的查询学习算法的理论。关于omega-自动机的约简，我们感兴趣的是减少确定性omega-自动机(在确定性过程中和直接在给定的确定性自动机上)规模的有效启发式算法，但我们也想研究精确最小化的方面，这比有限词上的确定性自动机要复杂得多。

The aim of this project is to study reduction and learning techniques for automata on infinite words (omega-automata). One central aspect is the development of algorithms for constructing omega-automata from finite sets of example words. This is a subject that has already been studied intensively for automata finite words but there are almost no results for infinite words. Furthermore, we want to advance the theory of query learning algorithms for omega-automata. Concerning reduction of omega-automata, we are interested in efficient heuristics for reducing the size of deterministic omega-automata (during determinization and directly on given deterministic automata), but we also want to study aspects of exact minimization, which is much more involved than for deterministic automata on finite words.