JOURNAL ARTICLE

Mean field team decision problems for Markov jump multiagent systems

Abstract

This paper studies the mean field team decision problem for multiagent systems with Markov jump parameters and coupled indices. By analyzing the centralized strategy of the team problem, we get a parameterized equation. Then by solving an optimal control problem in the augmented state space, we obtain the consistency equation, from which a set of distributed strategies is designed. By constructing the Lypunov function, we show that the closed-loop system is uniformly stable, and the set of distributed strategies is team-optimal.

Keywords:
Parameterized complexity Computer science Jump Markov decision process Mathematical optimization Consistency (knowledge bases) Multi-agent system Markov process Field (mathematics) State space Set (abstract data type) Function (biology) Decision problem Mathematics Artificial intelligence Algorithm

Metrics

2
Cited By
0.67
FWCI (Field Weighted Citation Impact)
31
Refs
0.74
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Distributed Control Multi-Agent Systems
Physical Sciences →  Computer Science →  Computer Networks and Communications
Distributed Sensor Networks and Detection Algorithms
Physical Sciences →  Computer Science →  Computer Networks and Communications
Game Theory and Applications
Social Sciences →  Decision Sciences →  Management Science and Operations Research

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