Performance, cost, and risk
What are the performance, cost, and risk impacts of implementing this product?
- Performance: Enables optimization and decision-making under uncertainty, such as scheduling of backup power for power generation systems. The product can scale better to larger models than other comparable methods. The model can also handle incomplete knowledge about the system being modeled.
- Cost: None specified.
- Risk: The model relies heavily on its underlying assumptions (i.e., discretizing a “continuous state space”). Improper discretization can lead to incorrect findings, and so discretization needs to be done carefully.
Implementation requirements
What capabilities would a business/organization/institution need to have to implement this product?
- Processes: None specified.
- Resources: Historical data of the system to model its behaviour as a Markov Chain.
- Competences: Knowledge of Markov Chain modeling to support the use of the tool.
- Technologies: Model checkers to verify model properties, simulation environments to run and use models.
Related works
- Badings et al. (2021). Balancing Wind and Batteries: Towards Predictive Verification of Smart Grids.
- Badings et al. (2022). Sampling-Based Robust Control of Autonomous Systems with Non-Gaussian Noise.
- Badings, Simão, Suilen, Jansen (2023). Decision-making under uncertainty: beyond probabilities.
- Badings, Romao, Abate, and Jansen (2023). Probabilities Are Not Enough: Formal Controller Synthesis for Stochastic Dynamical Models with Epistemic Uncertainty.
- Badings, Romao, Abate, Parker, et al. (2023). Robust Control for Dynamical Systems With Non-Gaussian Noise via Formal Abstractions.
- Badings, Ramao, Abate, and Jansen (2024). A Stability-Based Abstraction Framework for Reach-Avoid Control of Stochastic Dynamical Systems with Unknown Noise Distributions.
- Badings, Junges, et al. (2023). Efficient Sensitivity Analysis for Parametric Robust Markov Chains.
- Badings, Volk, Junges, et al. (2024). CTMCs with Imprecisely Timed Observations.
- Cubuktepe et al. (2021). Robust Finite-State Controllers for Uncertain POMDPs.
Contact information
For further inquiries regarding this product, feel free to get in touch with:
- Thom Badings, University of Oxford. thom [dot] badings [at] cs [dot] ox [dot] ac [dot] uk
- Mariëlle Stoelinga, University of Twente. m [dot] i [dot] a [dot] stoelinga [at] utwente [dot] nl
- Matthias Volk, Eindhoven University of Technology. m [dot] volk [at] tue [dot] nl
