1,015 research outputs found
Boolean Circuit Complexity of Regular Languages
In this paper we define a new descriptional complexity measure for
Deterministic Finite Automata, BC-complexity, as an alternative to the state
complexity. We prove that for two DFAs with the same number of states
BC-complexity can differ exponentially. In some cases minimization of DFA can
lead to an exponential increase in BC-complexity, on the other hand
BC-complexity of DFAs with a large state space which are obtained by some
standard constructions (determinization of NFA, language operations), is
reasonably small. But our main result is the analogue of the "Shannon effect"
for finite automata: almost all DFAs with a fixed number of states have
BC-complexity that is close to the maximum.Comment: In Proceedings AFL 2014, arXiv:1405.527
State Complexity of Reversals of Deterministic Finite Automata with Output
We investigate the worst-case state complexity of reversals of deterministic
finite automata with output (DFAOs). In these automata, each state is assigned
some output value, rather than simply being labelled final or non-final. This
directly generalizes the well-studied problem of determining the worst-case
state complexity of reversals of ordinary deterministic finite automata. If a
DFAO has states and possible output values, there is a known upper
bound of for the state complexity of reversal. We show this bound can be
reached with a ternary input alphabet. We conjecture it cannot be reached with
a binary input alphabet except when , and give a lower bound for the
case . We prove that the state complexity of reversal depends
solely on the transition monoid of the DFAO and the mapping that assigns output
values to states.Comment: 18 pages, 3 tables. Added missing affiliation/funding informatio
Learning Moore Machines from Input-Output Traces
The problem of learning automata from example traces (but no equivalence or
membership queries) is fundamental in automata learning theory and practice. In
this paper we study this problem for finite state machines with inputs and
outputs, and in particular for Moore machines. We develop three algorithms for
solving this problem: (1) the PTAP algorithm, which transforms a set of
input-output traces into an incomplete Moore machine and then completes the
machine with self-loops; (2) the PRPNI algorithm, which uses the well-known
RPNI algorithm for automata learning to learn a product of automata encoding a
Moore machine; and (3) the MooreMI algorithm, which directly learns a Moore
machine using PTAP extended with state merging. We prove that MooreMI has the
fundamental identification in the limit property. We also compare the
algorithms experimentally in terms of the size of the learned machine and
several notions of accuracy, introduced in this paper. Finally, we compare with
OSTIA, an algorithm that learns a more general class of transducers, and find
that OSTIA generally does not learn a Moore machine, even when fed with a
characteristic sample
On random primitive sets, directable NDFAs and the generation of slowly synchronizing DFAs
We tackle the problem of the randomized generation of slowly synchronizing
deterministic automata (DFAs) by generating random primitive sets of matrices.
We show that when the randomized procedure is too simple the exponent of the
generated sets is O(n log n) with high probability, thus the procedure fails to
return DFAs with large reset threshold. We extend this result to random
nondeterministic automata (NDFAs) by showing, in particular, that a uniformly
sampled NDFA has both a 2-directing word and a 3-directing word of length O(n
log n) with high probability. We then present a more involved randomized
algorithm that manages to generate DFAs with large reset threshold and we
finally leverage this finding for exhibiting new families of DFAs with reset
threshold of order .Comment: 31 pages, 9 figures. arXiv admin note: text overlap with
arXiv:1805.0672
Testing the Equivalence of Regular Languages
The minimal deterministic finite automaton is generally used to determine
regular languages equality. Antimirov and Mosses proposed a rewrite system for
deciding regular expressions equivalence of which Almeida et al. presented an
improved variant. Hopcroft and Karp proposed an almost linear algorithm for
testing the equivalence of two deterministic finite automata that avoids
minimisation. In this paper we improve the best-case running time, present an
extension of this algorithm to non-deterministic finite automata, and establish
a relationship between this algorithm and the one proposed in Almeida et al. We
also present some experimental comparative results. All these algorithms are
closely related with the recent coalgebraic approach to automata proposed by
Rutten
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