The current approach in IntervalMDP.jl, while efficient, may have some limitations that could be addressed for further improvement:
Support for Complex Specifications: Enhancing the package to support more complex specifications beyond reachability and reward optimization, such as temporal logic properties or probabilistic computation tree logic. This would broaden the applicability of IntervalMDP.jl to a wider range of probabilistic models.
Enhanced GPU Utilization: Optimizing the GPU-accelerated algorithms further to fully leverage the capabilities of modern GPUs, including exploring advanced CUDA features and optimizations to improve performance on larger models.
User-Friendly Interface: Improving the user interface and documentation to make it more intuitive and accessible for researchers and practitioners in the field of probabilistic analysis. Clearer guidelines and examples can enhance usability.
Scalability: Ensuring that the package can scale efficiently with the size and complexity of IMDPs, by implementing adaptive algorithms that adjust based on the model characteristics to maintain performance.
By addressing these potential limitations and incorporating these improvements, IntervalMDP.jl can become a more versatile and powerful tool for probabilistic analysis of complex systems.