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A timestamp-based log-linear algorithm for solving locally-informed multi-agent finite games
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AI
DOI：10.1016/j.eswa.2024.123677.png)
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Abstract
En 中文
In this paper, the distributed Nash equilibrium seeking problem for multi -agent finite games is considered. Considering the locally -informed communication structure, previous centralized game -theoretic learning dynamic methods are not feasible. To this end, we propose a distributed log -linear algorithm by introducing a timestamp-based communication mechanism that enables agents to acquire estimates of the true action profile from their neighborhoods. It is shown that the proposed distributed log -linear algorithm significantly outperforms the existing best response dynamics regarding both effectiveness and reliability. For illustration, a locally -informed multi -player Sudoku puzzle is introduced, where each cell is taken as a player, and each player fills numbers only based on the local information of its neighboring players. The simulation results validate the effectiveness of our algorithm in solving the locally -informed multi -player Sudoku puzzle. Our methodology provides a viable and benchmark approach to address the fully distributed locally -informed multi -agent joint decision -making problem.
Keywords:
Multi-agent cooperation
Sudoku puzzles
Distributed Nash equilibrium seeking
Finite potential games
Log-linear dynamics
Journal
IF:
7.5
Papers:
2.9W
Citations:
10.2W