To extend the framework to handle probabilistic properties of simulation models, especially those involving machine learning-based components, several considerations need to be taken into account.
Probabilistic Contracts: Introduce the concept of probabilistic contracts that can capture uncertainty and probabilistic behavior in simulation models. These contracts would define validity domains in terms of probability distributions and confidence intervals rather than deterministic thresholds.
Uncertainty Quantification: Develop methods to quantify uncertainty in simulation models, especially in the context of machine learning where predictions are inherently probabilistic. This could involve techniques such as Monte Carlo simulations or Bayesian inference.
Runtime Monitoring: Implement runtime monitors that can track and analyze the probabilistic outputs of simulation models during execution. These monitors would need to assess whether the behavior of the simulation models falls within the specified probabilistic validity domains.
Integration with Probabilistic Modeling: Integrate the framework with probabilistic modeling techniques to ensure that the simulation setups account for the inherent uncertainty in machine learning-based components. This could involve leveraging probabilistic graphical models or Bayesian networks.
Validation and Verification: Develop validation and verification methods that can handle probabilistic properties, ensuring that the simulation setups are not only valid in a deterministic sense but also account for the probabilistic nature of the models.
By incorporating these elements, the framework can effectively handle probabilistic properties in simulation models, particularly those associated with machine learning-based components.