Diverse Randomized Agents Vote to Win

NeurIPS 2014 Albert JiangLeandro Soriano MarcolinoAriel D. ProcacciaTuomas SandholmNisarg ShahMilind Tambe

We investigate the power of voting among diverse, randomized software agents. With teams of computer Go agents in mind, we develop a novel theoretical model of two-stage noisy voting that builds on recent work in machine learning... (read more)

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