Philipp Schlehuber-Caissier

Ω-regular energy problems

Abstract

We show how to efficiently solve problems involving a quantitative measure, here called energy, as well as a qualitative acceptance condition, expressed as a Büchi or Parity objective, in finite weighted automata and in one-clock weighted timed automata. Solving the former problem and extracting the corresponding witness is our main contribution and is handled by a modified version of the Bellman-Ford algorithm interleaved with Couvreur’s algorithm. The latter problem is handled via a reduction to the former relying on the corner-point abstraction. All our algorithms are freely available and implemented in a tool based on the open-source platforms TChecker and Spot.

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The reactive synthesis competition (SYNTCOMP): 2018-2021

Abstract

We report on the last four editions of the reactive synthesis competition (SYNTCOMP 2018–2021). We briefly describe the evaluation scheme and the experimental setup of SYNTCOMP. Then we introduce new benchmark classes that have been added to the SYNTCOMP library and give an overview of the participants of SYNTCOMP. Finally, we present and analyze the results of our experimental evaluations, including a ranking of tools with respect to quantity and quality—that is, the total size in terms of logic and memory elements—of solutions.

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Layered controller synthesis for dynamic multi-agent systems

Abstract

In this paper we present a layered approach for multi-agent control problem, decomposed into three stages, each building upon the results of the previous one. First, a high-level plan for a coarse abstraction of the system is computed, relying on parametric timed automata augmented with stopwatches as they allow to efficiently model simplified dynamics of such systems. In the second stage, the high-level plan, based on SMT-formulation, mainly handles the combinatorial aspects of the problem, provides a more dynamically accurate solution. These stages are collectively referred to as the SWA-SMT solver. They are correct by construction but lack a crucial feature: they cannot be executed in real time. To overcome this, we use SWA-SMT solutions as the initial training dataset for our last stage, which aims at obtaining a neural network control policy. We use reinforcement learning to train the policy, and show that the initial dataset is crucial for the overall success of the method.

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Dissecting ltlsynt

Abstract

ltlsynt is a tool for synthesizing a reactive circuit satisfying a specification expressed as an LTL formula. ltlsynt generally follows a textbook approach: the LTL specification is translated into a parity game whose winning strategy can be seen as a Mealy machine modeling a valid controller. This article details each step of this approach, and presents various refinements integrated over the years. Some of these refinements are unique to ltlsynt: for instance, ltlsynt supports multiple ways to encode a Mealy machine as an AIG circuit, features multiple simplification algorithms for the intermediate Mealy machine, and bypasses the usual game-theoretic approach for some subclasses of LTL formulas in favor of more direct constructions.

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The Mealy-machine reduction functions of Spot

Abstract

We present functions for reducing Mealy machines, initially detailed in our FORTE’22 article. These functions are now integrated into Spot 2.11.2, where they are used as part of the ltlsynt tool for reactive synthesis. Of course, since Spot is a library, these functions can also be used on their own, and we provide Python bindings for easy experiments. The reproducible capsule benchmarks these functions on Mealy machines from various sources, and compare them to the MeMin tool.

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Energy problems in finite and timed automata with Büchi conditions

Abstract

We show how to efficiently solve energy Büchi problems in finite weighted automata and in one-clock weighted timed automata. Solving the former problem is our main contribution and is handled by a modified version of Bellman-Ford interleaved with Couvreur’s algorithm. The latter problem is handled via a reduction to the former relying on the corner-point abstraction. All our algorithms are freely available and implemented in a tool based on the open-source tools TChecker and Spot.

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From Spot 2.0 to Spot 2.10: What’s new?

Abstract

Spot is a C++17 library for LTL and $\omega$-automata manipulation, with command-line utilities, and Python bindings. This paper summarizes its evolution over the past six years, since the release of Spot 2.0, which was the first version to support $\omega$-automata with arbitrary acceptance conditions, and the last version presented at a conference. Since then, Spot has been extended with several features such as acceptance transformations, alternating automata, games, LTL synthesis, and more. We also shed some lights on the data-structure used to store automata.

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The reactive synthesis competition (SYNTCOMP): 2018-2021

Abstract

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Effective reductions of Mealy machines

Abstract

We revisit the problem of reducing incompletely specified Mealy machines with reactive synthesis in mind. We propose two techniques: the former is inspired by the tool MeMin and solves the minimization problem, the latter is a novel approach derived from simulation-based reductions but may not guarantee a minimized machine. However, we argue that it offers a good enough compromise between the size of the resulting Mealy machine and performance. The proposed methods are benchmarked against MeMin on a large collection of test cases made of well-known instances as well as new ones.

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