基于似然估计的多智能体随机域学习约定
Learning Conventions in Multiagent Stochastic Domains using Likelihood Estimates
摘要：
完全合作的多智能体系统--在这些系统中，智能体共享一个联合效用模型--在人工智能中具有特殊的意义。一个关键的问题是，要确保各个代理人的行动是协调一致的，特别是在设置代理人是自主决策者。我们研究的方法来学习协调策略在随机域中，代理的行动是不直接观察到的其他。博弈论中最近的许多工作都采用了贝叶斯学习的观点来解决更一般的均衡选择问题，但倾向于假设行为可以被观察到。我们讨论的特殊问题时，出现的行动是不可观察的，包括对收敛速度的影响，和行动失败的概率和不对称性的影响。我们还使用似然估计作为一种手段，在我们的环境中推广虚构的游戏学习模型。最后，我们建议使用最大似然作为一种手段，从考虑中删除策略，收敛到一个传统的均衡，在这一点上学习和审议可以停止的目的。
英文摘要：
Fully cooperative multiagent systems - those in which agents share a joint utility model- is of special interest in AI. A key problem is that of ensuring that the actions of individual agents are coordinated, especially in settings where the agents are autonomous decision makers. We investigate approaches to learning coordinated strategies in stochastic domains where an agent's actions are not directly observable by others. Much recent work in game theory has adopted a Bayesian learning perspective to the more general problem of equilibrium selection, but tends to assume that actions can be observed. We discuss the special problems that arise when actions are not observable, including effects on rates of convergence, and the effect of action failure probabilities and asymmetries. We also use likelihood estimates as a means of generalizing fictitious play learning models in our setting. Finally, we propose the use of maximum likelihood as a means of removing strategies from consideration, with the aim of convergence to a conventional equilibrium, at which point learning and deliberation can cease.
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。
