Annual Report 2010 - Fachgruppe Informatik an der RWTH Aachen ...

exist practical algorithms for such games, these algorithms often perform badly on large scale
instances or are not applicable on infinite graphs. In such situations, where the given instances
are huge or the complexity of known algorithms is high, learning can offer a useful alternative
to develop algorithms that quickly identify small solutions if they exist.
As a first intermediate result we developed techniques to symbolically compute winning
strategies for reachability games on automatic graphs [15]. Using a proof-of-concept
implementation, we were able to prove that our techniques are competitive to known
approaches, such as SAT-solving or BDD-methods, on finite graphs. Moreover, on infinite
graphs we can guarantee to compute the solution, if we require that target to learn can be
represented symbolically. Our proof-of-concept implementation uses the learning framework
libalf, which is developed at the Chairs of Computer Science i2 and i7 [1].
Moreover, as a contrary area of interest in this project, we developed a novel learning
technique for visibly one-counter automata [16], motivated by an application for the
validation of XML streams.
Further Studies on Finite Automata
J. Olschewski, C. Löding
During his internship N. Bousquet from ENS Cachan worked with C. Löding on the
equivalence problem for Büchi automata. In [3] it is shown that for the class of strongly
unambiguous Büchi automata it is possible to solve the equivalence problem in polynomial
time. J. Olschewski and M. Ummels give a detailed complexity analysis of the problem of
finding shortest reset words in finite automata (see [19]).
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