Empirical Coordination with Two-Sided State Information and Correlated Source and State

TL;DR

This paper studies how autonomous agents can coordinate effectively in decentralized networks using empirical coordination, characterizing optimal solutions under various conditions with two-sided state information and correlated sources.

Contribution

It provides a comprehensive characterization of optimal empirical coordination solutions for different scenarios involving perfect channels, lossless decoding, and causal encoding/decoding.

Findings

Characterization of achievable joint distributions for perfect channels

Optimal solutions for lossless decoding scenarios

Analysis of causal encoding and decoding in coordination

Abstract

The coordination of autonomous agents is a critical issue for decentralized communication networks. Instead of transmitting information, the agents interact in a coordinated manner in order to optimize a general objective function. A target joint probability distribution is achievable if there exists a code such that the sequences of symbols are jointly typical. The empirical coordination is strongly related to the joint source-channel coding with two-sided state information and correlated source and state. This problem is also connected to state communication and is open for non-causal encoder and decoder. We characterize the optimal solutions for perfect channel, for lossless decoding, for independent source and channel, for causal encoding and for causal decoding.