arXiv: cs.AI preprintsInternational6 November 2026
Learning and Steering in Incomplete-Information Games
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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 and steer adaptive agents toward outcomes that maximize her utility using only trajectory feedback. The mediator knows the game form and her own objective, but not the agents' utilities. Moreover, she commits to nonnegative payments and provides recommendations and contexts, but receives only a single realized termina
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