83 research outputs found
On the Uniform Random Generation of Non Deterministic Automata Up to Isomorphism
In this paper we address the problem of the uniform random generation of non
deterministic automata (NFA) up to isomorphism. First, we show how to use a
Monte-Carlo approach to uniformly sample a NFA. Secondly, we show how to use
the Metropolis-Hastings Algorithm to uniformly generate NFAs up to isomorphism.
Using labeling techniques, we show that in practice it is possible to move into
the modified Markov Chain efficiently, allowing the random generation of NFAs
up to isomorphism with dozens of states. This general approach is also applied
to several interesting subclasses of NFAs (up to isomorphism), such as NFAs
having a unique initial states and a bounded output degree. Finally, we prove
that for these interesting subclasses of NFAs, moving into the Metropolis
Markov chain can be done in polynomial time. Promising experimental results
constitute a practical contribution.Comment: Frank Drewes. CIAA 2015, Aug 2015, Umea, Sweden. Springer, 9223,
pp.12, 2015, Implementation and Application of Automata - 20th International
Conferenc
Brzozowski Algorithm Is Generically Super-Polynomial Deterministic Automata
International audienceWe study the number of states of the minimal automaton of the mirror of a rational language recognized by a random deterministic automaton with n states. We prove that, for any d > 0, the probability that this number of states is greater than nd tends to 1 as n tends to infinity. As a consequence, the generic and average complexities of Brzozowski minimization algorithm are super-polynomial for the uniform distribution on deterministic automata
Should We Learn Probabilistic Models for Model Checking? A New Approach and An Empirical Study
Many automated system analysis techniques (e.g., model checking, model-based
testing) rely on first obtaining a model of the system under analysis. System
modeling is often done manually, which is often considered as a hindrance to
adopt model-based system analysis and development techniques. To overcome this
problem, researchers have proposed to automatically "learn" models based on
sample system executions and shown that the learned models can be useful
sometimes. There are however many questions to be answered. For instance, how
much shall we generalize from the observed samples and how fast would learning
converge? Or, would the analysis result based on the learned model be more
accurate than the estimation we could have obtained by sampling many system
executions within the same amount of time? In this work, we investigate
existing algorithms for learning probabilistic models for model checking,
propose an evolution-based approach for better controlling the degree of
generalization and conduct an empirical study in order to answer the questions.
One of our findings is that the effectiveness of learning may sometimes be
limited.Comment: 15 pages, plus 2 reference pages, accepted by FASE 2017 in ETAP
SAT-based Explicit LTL Reasoning
We present here a new explicit reasoning framework for linear temporal logic
(LTL), which is built on top of propositional satisfiability (SAT) solving. As
a proof-of-concept of this framework, we describe a new LTL satisfiability
tool, Aalta\_v2.0, which is built on top of the MiniSAT SAT solver. We test the
effectiveness of this approach by demonnstrating that Aalta\_v2.0 significantly
outperforms all existing LTL satisfiability solvers. Furthermore, we show that
the framework can be extended from propositional LTL to assertional LTL (where
we allow theory atoms), by replacing MiniSAT with the Z3 SMT solver, and
demonstrating that this can yield an exponential improvement in performance
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