图形化多智能体模型中的知识组合
Knowledge Combination in Graphical Multiagent Model
摘要：
图形多智能体模型（GMM）表示一组智能体行为的联合分布。关于代理行为的知识来源之一可能来自博弈论分析，如近年来开发的几种图形游戏表示所捕获的。基于博弈描述或其他与代理行为信念相关的知识来源，Gyndle将这种方法推广到表达任意分布。为了说明的灵活性，我们展示了游戏衍生的模型，允许概率偏离均衡，以及模型的基础上启发式的行动选择。我们研究了三种不同的方法，这些模型集成到一个单一的模型，代表组合的知识源。为了评估组合模型的预测性能，我们将强化学习过程产生的行为视为实际结果。我们发现，结合两个知识来源，使用任何方法，提供更好的预测比任何一个单独的来源。在组合方法中，混合数据优于本实证试验中研究的意见库和直接更新方法。
英文摘要：
A graphical multiagent model (GMM) represents a joint distribution over the behavior of a set of agents. One source of knowledge about agents' behavior may come from gametheoretic analysis, as captured by several graphical game representations developed in recent years. GMMs generalize this approach to express arbitrary distributions, based on game descriptions or other sources of knowledge bearing on beliefs about agent behavior. To illustrate the flexibility of GMMs, we exhibit game-derived models that allow probabilistic deviation from equilibrium, as well as models based on heuristic action choice. We investigate three different methods of integrating these models into a single model representing the combined knowledge sources. To evaluate the predictive performance of the combined model, we treat as actual outcome the behavior produced by a reinforcement learning process. We find that combining the two knowledge sources, using any of the methods, provides better predictions than either source alone. Among the combination methods, mixing data outperforms the opinion pool and direct update methods investigated in this empirical trial.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
