200 research outputs found
Verifying And Interpreting Neural Networks using Finite Automata
Verifying properties and interpreting the behaviour of deep neural networks
(DNN) is an important task given their ubiquitous use in applications,
including safety-critical ones, and their blackbox nature. We propose an
automata-theoric approach to tackling problems arising in DNN analysis. We show
that the input-output behaviour of a DNN can be captured precisely by a
(special) weak B\"uchi automaton of exponential size. We show how these can be
used to address common verification and interpretation tasks like adversarial
robustness, minimum sufficient reasons etc. We report on a proof-of-concept
implementation translating DNN to automata on finite words for better
efficiency at the cost of losing precision in analysis
09461 Abstracts Collection -- Algorithms and Applications for Next Generation SAT Solvers
From 8th to 13th November 2009, the Dagstuhl Seminar 09461 "Algorithms and Applications for Next Generation SAT Solvers" was held in Schloss Dagstuhl--Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts, slides or full papers are provided, if available
Büchi Objectives in Countable MDPs
We study countably infinite Markov decision processes with B\"uchi objectives, which ask to visit a given subset of states infinitely often. A question left open by T.P. Hill in 1979 is whether there always exist -optimal Markov strategies, i.e., strategies that base decisions only on the current state and the number of steps taken so far. We provide a negative answer to this question by constructing a non-trivial counterexample. On the other hand, we show that Markov strategies with only 1 bit of extra memory are sufficient
Polynomial Identification of omega-Automata
We study identification in the limit using polynomial time and data for
models of omega-automata. On the negative side we show that non-deterministic
omega-automata (of types Buchi, coBuchi, Parity, Rabin, Street, or Muller)
cannot be polynomially learned in the limit. On the positive side we show that
the omega-language classes IB, IC, IP, IR, IS, and IM, which are defined by
deterministic Buchi, coBuchi, Parity, Rabin, Streett, and Muller acceptors that
are isomorphic to their right-congruence automata, are identifiable in the
limit using polynomial time and data.
We give polynomial time inclusion and equivalence algorithms for
deterministic Buchi, coBuchi, Parity, Rabin, Streett, and Muller acceptors,
which are used to show that the characteristic samples for IB, IC, IP, IR, IS,
and IM can be constructed in polynomial time.
We also provide polynomial time algorithms to test whether a given
deterministic automaton of type X (for X in {B, C, P, R, S, M})is in the class
IX (i.e. recognizes a language that has a deterministic automaton that is
isomorphic to its right congruence automaton).Comment: This is an extended version of a paper with the same name that
appeared in TACAS2
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