Why Not? Solver-Grounded Certificates for Explainable Mission Planning
arXiv:2603.00469v1 Announce Type: new Abstract: Operators of Earth observation satellites need justifications for scheduling decisions: why a request was selected, rejected, or what changes would make it schedulable. Existing approaches construct post-hoc reasoning layers independent of the optimizer, risking non-causal attributions, incomplete constraint conjunctions, and solver-path dependence. We take a faithfulness-first approach: every explanation is a certificate derived from the optimization model itself: minimal infeasible subsets for rejections, tight constraints and contrastive trade-offs for selections, and inverse solves for what-if queries. On a scheduling instance with structurally distinct constraint interactions, certificates achieve perfect soundness with respect to the solver's constraint model (15/15 cited-constraint checks), counterfactual validity (7/7), and stability (Jaccard = 1.0 across 28 seed-pairs), while a post-hoc baseline produces non-causal attributions
arXiv:2603.00469v1 Announce Type: new Abstract: Operators of Earth observation satellites need justifications for scheduling decisions: why a request was selected, rejected, or what changes would make it schedulable. Existing approaches construct post-hoc reasoning layers independent of the optimizer, risking non-causal attributions, incomplete constraint conjunctions, and solver-path dependence. We take a faithfulness-first approach: every explanation is a certificate derived from the optimization model itself: minimal infeasible subsets for rejections, tight constraints and contrastive trade-offs for selections, and inverse solves for what-if queries. On a scheduling instance with structurally distinct constraint interactions, certificates achieve perfect soundness with respect to the solver's constraint model (15/15 cited-constraint checks), counterfactual validity (7/7), and stability (Jaccard = 1.0 across 28 seed-pairs), while a post-hoc baseline produces non-causal attributions in 29% of cases and misses constraint conjunctions in every multi-cause rejection. A scalability analysis up to 200 orders and 30 satellites confirms practical extraction times for operational batches.
Executive Summary
The article proposes a novel approach to explainable mission planning for Earth observation satellites, utilizing solver-grounded certificates to provide justifications for scheduling decisions. This faithfulness-first approach derives explanations directly from the optimization model, ensuring soundness, validity, and stability. The method outperforms post-hoc baselines, achieving perfect soundness and counterfactual validity in test cases, while also demonstrating practical scalability for operational batches.
Key Points
- ▸ Introduction of solver-grounded certificates for explainable mission planning
- ▸ Faithfulness-first approach to derive explanations from the optimization model
- ▸ Outperformance of post-hoc baselines in terms of soundness, validity, and stability
Merits
Improved Explainability
The proposed approach provides transparent and justifiable explanations for scheduling decisions, enhancing trust in the optimization model
Scalability
The method demonstrates practical extraction times for operational batches, making it suitable for real-world applications
Demerits
Complexity
The faithfulness-first approach may require significant computational resources and expertise to implement and interpret
Limited Generalizability
The method's effectiveness may be limited to specific types of optimization models or scheduling instances
Expert Commentary
The article presents a significant contribution to the field of explainable mission planning, offering a robust and scalable approach to deriving justifiable explanations from optimization models. The faithfulness-first approach ensures soundness and validity, addressing concerns surrounding post-hoc reasoning layers. However, the method's complexity and potential limitations in generalizability warrant further research and development. As the field continues to evolve, the integration of explainable AI and optimization modeling will play a crucial role in shaping the future of space exploration and satellite operations.
Recommendations
- ✓ Further research on the generalizability and applicability of the proposed approach to diverse optimization models and scheduling instances
- ✓ Development of user-friendly interfaces and tools to facilitate the implementation and interpretation of solver-grounded certificates in operational settings