# Scalable Dynamic Distributed Constraint Optimization with Metareasoning and Application to Continual Satellite Operations

arXiv: https://arxiv.org/abs/2601.06188 · HTML: https://mlsanity.com/paper/2601.06188/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 82 references that are in the map.
- First posted on arXiv 8 Jan 2026.

## Builds on (most cited first, 8 of 8)

- [A Survey of Methods for Automated Algorithm Configuration](https://mlsanity.com/paper/2202.01651/) (2022)
- [Distributed Constraint Optimization Problems and Applications: A Survey](https://mlsanity.com/paper/1602.06347/) (2016)
- [Agile Earth observation satellite scheduling over 20 years: formulations, methods and future directions](https://mlsanity.com/paper/2003.06169/) (2020)
- [A Maximum Independent Set Method for Scheduling Earth Observing Satellite Constellations](https://mlsanity.com/paper/2008.08446/) (2020)
- [Auction-based and Distributed Optimization Approaches for Scheduling Observations in Satellite Constellations with Exclusive Orbit Portions](https://mlsanity.com/paper/2106.03548/) (2021)
- [Planning, scheduling, and execution on the Moon: the CADRE technology demonstration mission](https://mlsanity.com/paper/2502.14803/) (2025)
- [Asynchronous Forward Bounding for Distributed COPs](https://mlsanity.com/paper/1401.3442/) (2014)
- [Effort Allocation for Deadline-Aware Task and Motion Planning: A Metareasoning Approach](https://mlsanity.com/paper/2410.05828/) (2024)

## Built on by (most cited first, 2 of 2)

- [Dynamic Targeting of Satellite Observations Using Supplemental Geostationary Satellite Data and Hierarchical Planning](https://mlsanity.com/paper/2603.06719/) (2026)
- [Distributed Constraint Optimization via Online Learning and Iterative Pricing with Application to Large-Scale Satellite Scheduling](https://mlsanity.com/paper/2607.25835/) (2026)

## Abstract (arXiv)

Dynamic distributed constraint optimization problems (DDCOPs) provide a general framework for coordinating autonomous agents in changing environments. However, existing DDCOP formulations do not adequately address settings where optimization and execution overlap, resources persist across time, and agents operate under limited computational and communication constraints. We extend the DDCOP model to address these challenges by introducing an execution-aware formulation, together with new algorithms and frameworks for efficiently computing solutions. We develop a general framework for metareasoning in DDCOPs, enabling agents to determine when the estimated benefit of recomputing solutions outweighs its computational cost. We further extend the neighborhood stochastic search algorithm to the dynamic setting, introducing dynamic incremental neighborhood stochastic search (D-NSS), a scalable decomposition-based DDCOP algorithm that efficiently repairs previous solutions in response to problem dynamics. We apply our methods to the real-world application of large-scale satellite scheduling. Deploying autonomy to satellites requires efficient computation and communication in the face of highly dynamic environments. We demonstrate that D-NSS stabilizes to high-quality solutions, outperforming standard DDCOP baselines in solution quality, computation time, and message volume, while our metareasoning framework successfully balances resource conservation with utility. These methods will support the NASA FAME mission, the largest in-space demonstration of distributed multi-agent AI to date.