Learning automata with side-effects

Automata learning has been successfully applied in the verification of hardware and software. The size of the automaton model learned is a bottleneck for scalability, and hence optimizations that enable learning of compact representations are important. This paper exploits monads, both as a mathematical structure and a programming construct, to design and prove correct a wide class of such optimizations. Monads enable the development of a new learning algorithm and correctness proofs, building upon a general framework for automata learning based on category theory. The new algorithm is parametric on a monad, which provides a rich algebraic structure to capture non-determinism and other side-effects. We show that this allows us to uniformly capture existing algorithms, develop new ones, and add optimizations

Symbolic Simulation of Dataflow Synchronous Programs with Timers

International audienceThe synchronous language Lustre and its descendants have long been used to program and model discrete controllers. Recent work shows how to mix discrete and continuous elements in a Lustre-like language called Zélus. The resulting hybrid programs are deterministic and can be simulated with a numerical solver. In this article, we focus on a subset of hybrid programs where continuous behaviors are expressed using timers, nondeterministic guards, and invariants, as in Timed Safety Automata. We propose a source-to-source compilation pass to generate discrete code that, coupled with standard operations on Difference-Bound Matrices, produces symbolic traces that each represent a set of concrete traces

Coalgebra learning via duality

Automata learning is a popular technique for inferring minimal automata through membership and equivalence queries. In this paper, we generalise learning to the theory of coalgebras. The approach relies on the use of logical formulas as tests, based on a dual adjunction between states and logical theories. This allows us to learn, e.g., labelled transition systems, using Hennessy-Milner logic. Our main contribution is an abstract learning algorithm, together with a proof of correctness and termination

Analysis of a clock synchronization protocol for wireless sensor networks

We study a clock synchronization protocol for the Chess WSN. First, we model the protocol as a network of timed automata and verify various instances using the Uppaal model checker. Next, we present a full parametric analysis of the protocol for the special case of cliques (networks with full connectivity), that is, we give constraints on the parameters that are both necessary and sufficient for correctness. These results have been checked using the proof assistant Isabelle. Finally, we present a negative result for the special case of line topologies: for any instantiation of the parameters, the protocol will eventually fail if the network grows. This result suggests a variation of the fundamental result of Fan and Lynch on gradient clock synchronization, where the synchronization eventually fails as the network diameter grows, for a setting with logical clocks whose value may also decrease