A novel learning algorithm for Büchi automata based on family of DFAs and classification trees

In this paper, we propose a novel algorithm to learn Büchi automaton from teacher who knows an ? -regular language. The learned can be nondeterministic or limit deterministic automaton. learning is based on formalism called family of DFAs (FDFAs) recently proposed by Angluin and Fisman. main catch that use classification tree structure instead the standard observation table structure. worst case storage space required our quadratically better than table-based We implement algorithms in library ROLL (Regular Omega Language Learning), which also consists other complete available literature. Experimental results show tree-based have best performance among others regarding number solved tasks.