论文标题
顺序合作贝叶斯推断
Sequential Cooperative Bayesian Inference
论文作者
论文摘要
从其他代理商学习时，通常会隐含地假设合作。合作意味着选择数据的代理商和代理商从数据中学习的目标与学习者推断预期的假设相同。人类和机器学习的最新模型证明了合作的可能性。我们通过顺序数据寻求贝叶斯代理商合作推断的基础理论结果。我们开发了新的方法，分析了顺序合作贝叶斯推断（SCBI）的一致性，收敛速度和稳定性。我们对有效性，样本效率和鲁棒性的分析表明，在特定情况下，合作不仅是可能的，而且通常在理论上具有良好的基础。我们讨论对人类和人机合作的影响。
Cooperation is often implicitly assumed when learning from other agents. Cooperation implies that the agent selecting the data, and the agent learning from the data, have the same goal, that the learner infer the intended hypothesis. Recent models in human and machine learning have demonstrated the possibility of cooperation. We seek foundational theoretical results for cooperative inference by Bayesian agents through sequential data. We develop novel approaches analyzing consistency, rate of convergence and stability of Sequential Cooperative Bayesian Inference (SCBI). Our analysis of the effectiveness, sample efficiency and robustness show that cooperation is not only possible in specific instances but theoretically well-founded in general. We discuss implications for human-human and human-machine cooperation.
