JANI: quantitative model and tool interaction
The formal analysis of critical systems is supported by a vast space of modelling formalisms
and tools. The variety of incompatible formats and tools however poses a significant …
and tools. The variety of incompatible formats and tools however poses a significant …
An ordered approach to solving parity games in quasi polynomial time and quasi linear space
Parity games play an important role in model checking and synthesis. In their paper, Calude
et al. have recently shown that these games can be solved in quasi-polynomial time. We …
et al. have recently shown that these games can be solved in quasi-polynomial time. We …
The 2019 Comparison of Tools for the Analysis of Quantitative Formal Models: (QComp 2019 Competition Report)
Quantitative formal models capture probabilistic behaviour, real-time aspects, or general
continuous dynamics. A number of tools support their automatic analysis with respect to …
continuous dynamics. A number of tools support their automatic analysis with respect to …
On correctness, precision, and performance in quantitative verification: QComp 2020 competition report
Quantitative verification tools compute probabilities, expected rewards, or steady-state
values for formal models of stochastic and timed systems. Exact results often cannot be …
values for formal models of stochastic and timed systems. Exact results often cannot be …
Mungojerrie: Linear-time objectives in model-free reinforcement learning
Mungojerrie is an extensible tool that provides a framework to translate linear-time
objectives into reward for reinforcement learning (RL). The tool provides convergent RL …
objectives into reward for reinforcement learning (RL). The tool provides convergent RL …
Model-free reinforcement learning for stochastic parity games
This paper investigates the use of model-free reinforcement learning to compute the optimal
value in two-player stochastic games with parity objectives. In this setting, two decision …
value in two-player stochastic games with parity objectives. In this setting, two decision …
EPMC Gets Knowledge in Multi-agent Systems
In this paper, we present epmc, an extendible probabilistic model checker. epmc has a small
kernel, and is designed modularly. It supports discrete probabilistic models such as Markov …
kernel, and is designed modularly. It supports discrete probabilistic models such as Markov …
Quasipolynomial computation of nested fixpoints
D Hausmann, L Schröder - … Conference on Tools and Algorithms for the …, 2021 - Springer
It is well-known that the winning region of a parity game with n nodes and k priorities can be
computed as ak-nested fixpoint of a suitable function; straightforward computation of this …
computed as ak-nested fixpoint of a suitable function; straightforward computation of this …
Mungojerrie: Reinforcement learning of linear-time objectives
Reinforcement learning synthesizes controllers without prior knowledge of the system. At
each timestep, a reward is given. The controllers optimize the discounted sum of these …
each timestep, a reward is given. The controllers optimize the discounted sum of these …
Synthesising strategy improvement and recursive algorithms for solving 2.5 player parity games
5 player parity games combine the challenges posed by 2.5 player reachability games and
the qualitative analysis of parity games. These two types of problems are best approached …
the qualitative analysis of parity games. These two types of problems are best approached …