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New algorithm learns and steers agents in incomplete-information games

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel algorithm called Learn-Compute-Steer to address scenarios in multi-agent systems where a mediator aims to influence agent behavior without full knowledge of their utilities. This algorithm learns the unknown game dynamics and guides adaptive agents toward outcomes that align with the mediator's objectives, using only trajectory feedback. The approach achieves sample complexity matching theoretical lower bounds and demonstrates effectiveness in online learning, equilibrium computation, and steering toward mediator-optimal extensive-form correlated equilibria. AI

IMPACT Introduces a new method for controlling multi-agent systems with incomplete information, potentially impacting AI research in strategic decision-making.

RANK_REASON Academic paper detailing a new algorithm for game theory. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New algorithm learns and steers agents in incomplete-information games

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Academic paper detailing a new algorithm for game theory. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Asr{\i}n Efe Yorulmaz, Tamer Ba\c{s}ar ·

    Learning and Steering in Incomplete-Information Games

    arXiv:2611.07589v1 Announce Type: cross Abstract: In many modern multi-agent systems, a mediator seeks to influence strategic decision dynamics using only outcome-level feedback and without knowing agents' utilities. In this work, we study how a mediator can learn an unknown game…

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