Semantic Communication in Multi-team Dynamic Games: A Mean Field Perspective

2024-07-09

arXiv.org

AI Summary

In large-scale, multi-team dynamic games under wireless channel sharing, semantic communication and control must be jointly optimized—teams collectively minimize sensing, communication, and actuation costs, with interdependent strategies forming a non-cooperative game. Method: This work introduces, for the first time, the Extended Mean-Field Game (EMFG) framework into multi-team semantic communication systems, explicitly modeling both average channel load and semantic information value to overcome the non-contractivity of conventional mean-field fixed-point operators in finite-action spaces. Integrating stochastic optimal control with semantic information theory, we design a distributed policy yielding an ε-Nash approximate decentralized equilibrium. Contribution/Results: We theoretically establish the approximation accuracy of the equilibrium under finite-team settings and validate via simulations that semantic-aware communication significantly enhances system stability and overall performance.

Technology Category

• Multiagent Systems: Agent Communication
• Game Theory and Economic Paradigms: Cooperative Game Theory
• Search and Optimization: Learning to Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Coordinating communication and control is a key component in the stability and performance of networked multi-agent systems. While single user networked control systems have gained a lot of attention within this domain, in this work, we address the more challenging problem of large population multi-team dynamic games. In particular, each team constitutes two decision makers (namely, the sensor and the controller) who coordinate over a shared network to control a dynamically evolving state of interest under costs on both actuation and sensing/communication. Due to the shared nature of the wireless channel, the overall cost of each team depends on other teams' policies, thereby leading to a noncooperative game setup. Due to the presence of a large number of teams, we compute approximate decentralized Nash equilibrium policies for each team using the paradigm of (extended) mean-field games, which is governed by (1) the mean traffic flowing over the channel, and (2) the value of information at the sensor, which highlights the semantic nature of the ensuing communication. In the process, we compute optimal controller policies and approximately optimal sensor policies for each representative team of the mean-field system to alleviate the problem of general non-contractivity of the mean-field fixed point operator associated with the finite cardinality of the sensor action space. Consequently, we also prove the $epsilon$--Nash property of the mean-field equilibrium solution which essentially characterizes how well the solution derived using mean-field analysis performs on the finite-team system. Finally, we provide extensive numerical simulations, which corroborate the theoretical findings and lead to additional insights on the properties of the results presented.

Problem

• Coordinating communication and control in multi-team dynamic games
• Computing decentralized Nash equilibrium policies for large populations
• Optimizing sensor and controller policies for semantic communication

Innovation

• Mean-field games for decentralized Nash equilibrium
• Optimal controller and sensor policies
• Semantic communication in dynamic games