mlsanityData to 14 Oct 2026

MULTIGAIN 2.0: MDP controller synthesis for multiple mean-payoff, LTL and steady-state constraints

arXiv 2305.16752 ↗First posted 26 May 2023cs.AI cs.LOcited by 1

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Abstract (arXiv)

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 multi-dimensional 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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 5 of its 17 references that are in the map. First posted on arXiv 26 May 2023.

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5 of 17 references.

References · 5

  1. 2017Probabilistic Motion Planning under Temporal Tasks and Soft Constraintscited by 9
  2. 2015MultiGain: A controller synthesis tool for MDPs with multiple mean-payoff objectivescited by 3
  3. 2020Multi-objective Optimization of Long-run Average and Total Rewardscited by 2
  4. 2021LTL-Constrained Steady-State Policy Synthesiscited by 2
  5. 2021Controller Synthesis for Omega-Regular and Steady-State Specificationscited by 2

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