# Distributed Estimation with Information-Seeking Control in Agent Network

arXiv: https://arxiv.org/abs/1408.3732 · HTML: https://mlsanity.com/paper/1408.3732/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Aug 2014.

## Built on by (most cited first, 3 of 3)

- [Neural Enhanced Belief Propagation for Cooperative Localization](https://mlsanity.com/paper/2105.12903/) (2021)
- [ISEE.U: Distributed online active target localization with unpredictable targets](https://mlsanity.com/paper/2210.09107/) (2022)
- [A Cognitive Framework for Autonomous Agents: Toward Human-Inspired Design](https://mlsanity.com/paper/2601.16648/) (2026)

## Abstract (arXiv)

We introduce a distributed, cooperative framework and method for Bayesian estimation and control in decentralized agent networks. Our framework combines joint estimation of time-varying global and local states with information-seeking control optimizing the behavior of the agents. It is suited to nonlinear and non-Gaussian problems and, in particular, to location-aware networks. For cooperative estimation, a combination of belief propagation message passing and consensus is used. For cooperative control, the negative posterior joint entropy of all states is maximized via a gradient ascent. The estimation layer provides the control layer with probabilistic information in the form of sample representations of probability distributions. Simulation results demonstrate intelligent behavior of the agents and excellent estimation performance for a simultaneous self-localization and target tracking problem. In a cooperative localization scenario with only one anchor, mobile agents can localize themselves after a short time with an accuracy that is higher than the accuracy of the performed distance measurements.