Adaptation of Parameters in Heterogeneous Multi-agent Systems

2025-08-31

AI Summary

To address the challenge of achieving exact state consensus in heterogeneous multi-agent systems due to intrinsic differences in node dynamics, this paper proposes a strongly coupled adaptive mechanism that requires no explicit parameter exchange. The method leverages synchronization errors as driving signals to asymptotically align internal tunable parameters across agents, thereby enabling system-wide asymptotic homogenization and exact state consensus while preserving individual heterogeneity. It employs linearly parameterized vector field modeling, hybrid dynamical analysis, and rigorous verification of the persistent excitation condition. Theoretical analysis proves that, under persistent excitation, all agent parameters converge to a common value and states achieve asymptotic synchronization. The key innovation lies in implicitly coordinating parameters through reused coupling signals—eliminating the need for explicit parameter communication—thus significantly enhancing interpretability and practical feasibility in resource-constrained scenarios such as biological swarms and social networks.

Technology Category

• Multiagent Systems: Adversarial Agents
• Intelligent Robots: State Estimation
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

This paper proposes an adaptation mechanism for heterogeneous multi-agent systems to align the agents' internal parameters, based on enforced consensus through strong couplings. Unlike homogeneous systems, where exact consensus is attainable, the heterogeneity in node dynamics precludes perfect synchronization. Nonetheless, previous work has demonstrated that strong coupling can induce approximate consensus, whereby the agents exhibit emergent collective behavior governed by the so-called blended dynamics. Building on this observation, we introduce an adaptation law that gradually aligns the internal parameters of agents without requiring direct parameter communication. The proposed method reuses the same coupling signal employed for state synchronization, which may result in a biologically or sociologically plausible adaptation process. Under a persistent excitation condition, we prove that the linearly parametrized vector fields of the agents converge to each other, thereby making the dynamics asymptotically homogeneous, and leading to exact consensus of the state variables.

Problem

• Aligning internal parameters in heterogeneous multi-agent systems
• Achieving consensus without direct parameter communication
• Making agent dynamics asymptotically homogeneous through adaptation

Innovation

• Adaptation mechanism for heterogeneous multi-agent systems
• Enforced consensus through strong couplings
• Reuses coupling signal without parameter communication