Dario Bauso

University of Sheffield, University of Groningen

Papers

Total Citations

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About

Dario Bauso’s research lies at the dynamic intersection of game theory, control systems, and multi-agent robotics, with a central focus on enabling cooperative behavior under uncertainty. His major contributions include pioneering the application of game-theoretic learning algorithms to robot populations, where he models interactions using simple yet powerful reward and cost structures. In his influential 2016 work, Bauso established convergence and equilibrium properties for regret-based learning, proposing novel models that allow robotic teams to self-organize and cooperate without centralized control. Building on this, his 2020 research introduced a groundbreaking variant of fictitious play that integrates multi-model adaptive filters, enabling robots to estimate the strategies of their peers in real-time. This innovation directly addresses the challenge of coordination in uncertain environments, providing a robust framework for distributed decision-making. While his most-cited papers currently hold 2 citations each, their conceptual depth and practical relevance to swarm robotics and autonomous systems signal a growing influence. Bauso’s work is essential reading for researchers seeking rigorous, scalable solutions to multi-agent coordination problems.