# Contingency Planning Using Bi-level Markov Decision Processes for Space Missions

arXiv: https://arxiv.org/abs/2402.16342 · HTML: https://mlsanity.com/paper/2402.16342/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 20 references that are in the map.
- First posted on arXiv 26 Feb 2024.

## Built on by (most cited first, 1 of 1)

- [Robustness Analysis of POMDP Policies to Observation Perturbations](https://mlsanity.com/paper/2604.21256/) (2026)

## Abstract (arXiv)

This work focuses on autonomous contingency planning for scientific missions by enabling rapid policy computation from any off-nominal point in the state space in the event of a delay or deviation from the nominal mission plan. Successful contingency planning involves managing risks and rewards, often probabilistically associated with actions, in stochastic scenarios. Markov Decision Processes (MDPs) are used to mathematically model decision-making in such scenarios. However, in the specific case of planetary rover traverse planning, the vast action space and long planning time horizon pose computational challenges. A bi-level MDP framework is proposed to improve computational tractability, while also aligning with existing mission planning practices and enhancing explainability and trustworthiness of AI-driven solutions. We discuss the conversion of a mission planning MDP into a bi-level MDP, and test the framework on RoverGridWorld, a modified GridWorld environment for rover mission planning. We demonstrate the computational tractability and near-optimal policies achievable with the bi-level MDP approach, highlighting the trade-offs between compute time and policy optimality as the problem's complexity grows. This work facilitates more efficient and flexible contingency planning in the context of scientific missions.