# Information-Theoretic Abstractions for Resource-Constrained Agents via Mixed-Integer Linear Programming

arXiv: https://arxiv.org/abs/2102.10015 · HTML: https://mlsanity.com/paper/2102.10015/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 27 references that are in the map.
- First posted on arXiv 19 Feb 2021.

## Builds on (most cited first, 4 of 4)

- [Deep Variational Information Bottleneck](https://mlsanity.com/paper/1612.00410/) (2016)
- [Nonlinear Information Bottleneck](https://mlsanity.com/paper/1705.02436/) (2017)
- [Anytime Stereo Image Depth Estimation on Mobile Devices](https://mlsanity.com/paper/1810.11408/) (2018)
- [Q-Search Trees: An Information-Theoretic Approach Towards Hierarchical Abstractions for Agents with Computational Limitations](https://mlsanity.com/paper/1910.00063/) (2019)

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

- [A Linear Programming Approach for Resource-Aware Information-Theoretic Tree Abstractions](https://mlsanity.com/paper/2208.04220/) (2022)
- [Communication-aware Hierarchical Map Compression of Time-Varying Environments for Mobile Robots](https://mlsanity.com/paper/2504.10751/) (2025)

## Abstract (arXiv)

In this paper, a mixed-integer linear programming formulation for the problem of obtaining task-relevant, multi-resolution, graph abstractions for resource-constrained agents is presented. The formulation leverages concepts from information-theoretic signal compression, specifically the information bottleneck (IB) method, to pose a graph abstraction problem as an optimal encoder search over the space of multi-resolution trees. The abstractions emerge in a task-relevant manner as a function of agent information-processing constraints, and are not provided to the system a priori. We detail our formulation and show how the problem can be realized as an integer linear program. A non-trivial numerical example is presented to demonstrate the utility in employing our approach to obtain hierarchical tree abstractions for resource-limited agents.