927 research outputs found
Learning Deterministic Finite Automata from Infinite Alphabets
We proposes an algorithm to learn automata infinite alphabets, or at least too large to enumerate. We apply it to define a generic model intended for regression, with transitions constrained by intervals over the alphabet. The algorithm is based on the Red \& Blue framework for learning from an input sample. We show two small case studies where the alphabets are respectively the natural and real numbers, and show how nice properties of automata models like interpretability and graphical representation transfer to regression where typical models are hard to interpret
Automata theory in nominal sets
We study languages over infinite alphabets equipped with some structure that
can be tested by recognizing automata. We develop a framework for studying such
alphabets and the ensuing automata theory, where the key role is played by an
automorphism group of the alphabet. In the process, we generalize nominal sets
due to Gabbay and Pitts
Random Generation and Enumeration of Accessible Determinisitic Real-time Pushdown Automata
This papers presents a general framework for the uniform random generation of
deterministic real-time accessible pushdown automata. A polynomial time
algorithm to randomly generate a pushdown automaton having a fixed stack
operations total size is proposed. The influence of the accepting condition
(empty stack, final state) on the reachability of the generated automata is
investigated.Comment: Frank Drewes. CIAA 2015, Aug 2015, Umea, Sweden. Springer, 9223,
pp.12, 2015, Implementation and Application of Automata - 20th International
Conferenc
Residual Nominal Automata
Nominal automata are models for accepting languages over infinite alphabets.
In this paper we refine the hierarchy of nondeterministic nominal automata, by
developing the theory of residual nominal automata. In particular, we show that
they admit canonical minimal representatives, and that the universality problem
becomes decidable. We also study exact learning of these automata, and settle
questions that were left open about their learnability via observations
DEQ:Equivalence Checker for Deterministic Register Automata
Register automata are one of the most studied automata models over infinite alphabets with applications in learning, systems modelling
and program verification. We present an equivalence checker for deterministic register automata, called DEQ, based on a recent polynomial-time
algorithm that employs group-theoretic techniques to achieve succinct
representations of the search space. We compare the performance of our
tool to other available implementations, notably in the learning library
RALib and nominal frameworks LOIS and NLambda
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