Joshua Moerman
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View article: Output-decomposed Learning of Mealy Machines
Output-decomposed Learning of Mealy Machines Open
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 compositional learning algorithm by Labbaf et al. (2023). When p…
View article: Orbit-Finite-Dimensional Vector Spaces and Weighted Register Automata
Orbit-Finite-Dimensional Vector Spaces and Weighted Register Automata Open
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 common generalization of weighted automata and register automat…
View article: Residuality and Learning for Nondeterministic Nominal Automata
Residuality and Learning for Nondeterministic Nominal Automata Open
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 particularly challenging for languages recognised by nondeterministic …
View article: Gradient-Descent for Randomized Controllers under Partial Observability
Gradient-Descent for Randomized Controllers under Partial Observability Open
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 system performance, yet they are intricate to get right. The …
View article: Orbit-Finite-Dimensional Vector Spaces and Weighted Register Automata
Orbit-Finite-Dimensional Vector Spaces and Weighted Register Automata Open
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 common generalization of weighted automata and register automat…
View article: Residuality and Learning for Nondeterministic Register Automata
Residuality and Learning for Nondeterministic Register Automata Open
We are motivated by the following question: which data languages admit an active learning algorithm? This question was left open in previous work, and is particularly challenging for languages recognised by nondeterministic automata. To an…
View article: Separation and Renaming in Nominal Sets
Separation and Renaming in Nominal Sets Open
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 paper, nominal sets are related to nominal renaming sets, which…
View article: Learning Product Automata
Learning Product Automata Open
In this paper we give an optimization for active learning algorithms, applicable to learning Moore machines where the output comprises several observables. These machines can be decomposed themselves by projecting on each observable, resul…
View article: Residual Nominal Automata
Residual Nominal Automata Open
We are motivated by the following question: which nominal languages admit an active learning algorithm? This question was left open in previous work, and is particularly challenging for languages recognised by nondeterministic automata. To…
View article: n-Complete test suites for IOCO
n-Complete test suites for IOCO Open
An n-complete test suite for automata guarantees to detect all faulty implementations with a bounded number of states. We propose a construction of such a test suite for ioco conformance on labeled transition systems, which we derive from …
View article: Learning Product Automata
Learning Product Automata Open
In this paper we give an optimization for active learning algorithms, applicable to learning Moore machines where the output comprises several observables. These machines can be decomposed themselves by projecting on each observable, resul…
View article: Complementing Model Learning with Mutation-Based Fuzzing
Complementing Model Learning with Mutation-Based Fuzzing Open
An ongoing challenge for learning algorithms formulated in the Minimally Adequate Teacher framework is to efficiently obtain counterexamples. In this paper we compare and combine conformance testing and mutation-based fuzzing methods for o…