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Omar Adalat

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#machine learning Preprint Oct 2026

Learning Rational Strategies for Temporal Objectives in Multi-Agent Systems

We study rational synthesis for stochastic multi-agent systems with unknown dynamics, where each agent is equipped with its own probabilistic temporal objective specified using Probabilistic Linear Temporal Logic on Finite Traces ($\mathsf{PLTL}_f}$). We impose solution concepts (e.g., Nash equilibrium, subgame-perfect...

Omar Adalat, Edwin Hamel-De le Court, Francesco Belardinelli · 0 citations

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