# An attention model for the formation of collectives in real-world domains

arXiv: https://arxiv.org/abs/2205.00215 · HTML: https://mlsanity.com/paper/2205.00215/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 42 references that are in the map.
- First posted on arXiv 30 Apr 2022.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Attention, Learn to Solve Routing Problems!](https://mlsanity.com/paper/1803.08475/) (2018)
- [Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizon](https://mlsanity.com/paper/1811.06128/) (2018)
- [Learning to Solve Large-Scale Security-Constrained Unit Commitment Problems](https://mlsanity.com/paper/1902.01697/) (2019)
- [Coalition Structure Generation over Graphs](https://mlsanity.com/paper/1401.6428/) (2014)
- [Synergistic Team Composition: A Computational Approach to Foster Diversity in Teams](https://mlsanity.com/paper/1909.11994/) (2019)
- [The First AI4TSP Competition: Learning to Solve Stochastic Routing Problems](https://mlsanity.com/paper/2201.10453/) (2022)
- [Algorithms for Graph-Constrained Coalition Formation in the Real World](https://mlsanity.com/paper/1612.04299/) (2016)

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

- [Metaheuristics and Large Language Models Join Forces: Toward an Integrated Optimization Approach](https://mlsanity.com/paper/2405.18272/) (2024)
- [Exploiting Constraint Reasoning to Build Graphical Explanations for Mixed-Integer Linear Programming](https://mlsanity.com/paper/2507.13007/) (2025)

## Abstract (arXiv)

We consider the problem of forming collectives of agents for real-world applications aligned with Sustainable Development Goals (e.g., shared mobility, cooperative learning). We propose a general approach for the formation of collectives based on a novel combination of an attention model and an integer linear program (ILP). In more detail, we propose an attention encoder-decoder model that transforms a collective formation instance to a weighted set packing problem, which is then solved by an ILP. Results on two real-world domains (i.e., ridesharing and team formation for cooperative learning) show that our approach provides solutions that are comparable (in terms of quality) to the ones produced by state-of-the-art approaches specific to each domain. Moreover, our solution outperforms the most recent general approach for forming collectives based on Monte Carlo tree search.