Publication details
MULTIGAIN 2.0: MDP controller synthesis for multiple mean-payoff, LTL and steady-state constraints✱
| Authors | |
|---|---|
| Year of publication | 2024 |
| Type | Article in Proceedings |
| Conference | Proceedings of the 27th ACM International Conference on Hybrid Systems: Computation and Control, HSCC 2024 |
| MU Faculty or unit | |
| Citation | |
| Doi | https://doi.org/10.1145/3641513.3650135 |
| Keywords | Markov decision process; quantitative verification; probabilistic model checking; controller synthesis |
| Description | We present MultiGain 2.0, a major extension to the controller synthesis tool MultiGain, built on top of the probabilistic model checker PRISM. This new version extends MultiGain’s multi-objective capabilities, by allowing for the formal verification and synthesis of controllers for probabilistic systems with multidimensional long-run average reward structures, steady-state constraints, and linear temporal logic properties. Additionally, MultiGain 2.0 can modify the underlying linear program to prevent unbounded-memory and other unintuitive solutions and visualizes Pareto curves, in the two- and three-dimensional cases, to facilitate trade-off analysis in multi-objective scenarios. |
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