We present L#, a new and simple approach to active automata learning. Instead of focusing
on equivalence of observations, like the L∗ algorithm and its descendants, L# takes a …

Deep learning, despite its remarkable achievements, is still a young field. Like the early
stages of many scientific disciplines, it is marked by the discovery of new phenomena, ad …

Abstract We use Category Theory to interpret the family of algorithms for inference of DFAs
that work by merging states. This interpretation allows us to characterize the structure of the …

In this paper, we present a categorical approach to learning automata over words, in the
sense of the $ L^* $-algorithm of Angluin. This yields a new generic $ L^* $-like algorithm …

Automata learning is a technique that has successfully been applied in verification, with the
automaton type varying depending on the application domain. Adaptations of automata …

We describe the canonical weak distributive law δ: SP→ PS of the powerset monad P over
the S-left-semimodule monad S, for a class of semirings S. We show that the composition of …

Abstract models of embedded systems are useful for various tasks, ranging from diagnosis,
through testing to monitoring at run-time. However, deriving a model for an unknown system …

We study monoidal transducers, transition systems arising as deterministic automata whose
transitions also produce outputs in an arbitrary monoid, for instance allowing outputs to …

Learning Pomset Automata. Page 524 Learning Pomset Automata ⋆ Gerco van Heerdt1(),
Tobias Kappé2, Jurriaan Rot3, and Alexandra Silva1 1 University College London, London, UK …

Cyber-Physical Systems (CPS) are often black-box systems, ie, knowledge of the inner
workings or a system model is not available. Nevertheless, models of CPS are needed for …