22 citations · 44 across the 8 of their papers we have counts for
11 papers
Output-decomposed Learning of Mealy Machines
Rick Koenders, Joshua Moerman
We present an active automata learning algorithm which learns a decomposition of a finite state machine, based on projecting onto individual outputs. This is dual to a recent compo…
Gradient-Descent for Randomized Controllers under Partial Observability
Linus Heck, Jip Spel, Sebastian Junges +2
Randomization is a powerful technique to create robust controllers, in particular in partially observable settings. The degrees of randomization have a significant impact on the sy…
Orbit-Finite-Dimensional Vector Spaces and Weighted Register Automata
Mikołaj Bojańczyk, Joanna Fijalkow, Bartek Klin +1
We develop a theory of vector spaces spanned by orbit-finite sets. Using this theory, we give a decision procedure for equivalence of weighted register automata, which are the comm…
Generating Functions for Probabilistic Programs
Lutz Klinkenberg, Kevin Batz, Benjamin Lucien Kaminski +3
This paper investigates the usage of generating functions (GFs) encoding measures over the program variables for reasoning about discrete probabilistic programs. To that end, we de…
Residuality and Learning for Nondeterministic Nominal Automata
Joshua Moerman, Matteo Sammartino
We are motivated by the following question: which data languages admit an active learning algorithm? This question was left open in previous work by the authors, and is particularl…
Separation and Renaming in Nominal Sets
Joshua Moerman, Jurriaan Rot
Nominal sets provide a foundation for reasoning about names. They are used primarily in syntax with binders, but also, e.g., to model automata over infinite alphabets. In this pape…