论文标题
多代理系统中的分布式可能学习
Distributed Possibilistic Learning in Multi-Agent Systems
论文作者
论文摘要
可能性理论被认为是多代理系统和机器人群中分布式学习的不确定性表示框架。特别是，我们调查了其在最佳N问题上的应用，在该问题中，目标是通过个人之间的本地互动和有限的直接反馈来确定n个选项中最高质量的最高质量。在这种情况下，我们声称可能性理论提供了有效的机制，代理人可以通过这些机制来了解世界的状态，这可以使他们能够通过改变自己的信念的不精确度来处理他们和其他人之间相信的不一致之处。我们介绍了将可能性理论应用于最佳问题的代理商人群的离散时间模型。然后，使用模拟实验来研究这种情况下可能性理论的准确性，以及在不同数量的直接证据下对噪声的鲁棒性。最后，我们将在这种情况下的可能性理论与类似的概率方法进行了比较。
Possibility theory is proposed as an uncertainty representation framework for distributed learning in multi-agent systems and robot swarms. In particular, we investigate its application to the best-of-n problem where the aim is for a population of agents to identify the highest quality out of n options through local interactions between individuals and limited direct feedback from the environment. In this context we claim that possibility theory provides efficient mechanisms by which an agent can learn about the state of the world, and which can allow them to handle inconsistencies between what they and others believe by varying the level of imprecision of their own beliefs. We introduce a discrete time model of a population of agents applying possibility theory to the best-of-n problem. Simulation experiments are then used to investigate the accuracy of possibility theory in this context as well as its robustness to noise under varying amounts of direct evidence. Finally, we compare possibility theory in this context with a similar probabilistic approach.
