图像参考游戏中概念理解的建模
Modeling Conceptual Understanding in Image Reference Games
摘要：
一个与大量其他智能体交互的智能体需要意识到，他们对世界的理解可能会有所不同。此外，他们用来感知的机器可能本质上不同，就像人类和机器之间的情况一样。在这项工作中，我们提出了一个扬声器和人口的听众之间的图像参考游戏，推理的概念，其他代理可以理解的是必要的和模型制定与此能力。我们专注于推理他人的概念理解，以及适应新的游戏伙伴和处理感知机制的差异。我们在三个基准图像/属性数据集上的实验表明，我们的学习器确实编码了与其他代理的理解直接相关的信息，并且利用这些信息对于最大化游戏性能至关重要。
英文摘要：
An agent who interacts with a wide population of other agents needs to be aware that there may be variations in their understanding of the world. Furthermore, the machinery which they use to perceive may be inherently different, as is the case between humans and machines. In this work, we present both an image reference game between a speaker and a population of listeners where reasoning about the concepts other agents can comprehend is necessary and a model formulation with this capability. We focus on reasoning about the conceptual understanding of others, as well as adapting to novel gameplay partners and dealing with differences in perceptual machinery. Our experiments on three benchmark image/attribute datasets suggest that our learner indeed encodes information directly pertaining to the understanding of other agents, and that leveraging this information is crucial for maximizing gameplay performance.
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。
