关注信息而忽略信息：指称游戏中的沟通策略
Focus on What's Informative and Ignore What's not: Communication Strategies in a Referential Game
摘要：
多智能体合作的研究表明，人工智能体能够学习玩一个简单的参考游戏，同时开发一个共享的词典。这个词典不容易分析，因为它没有显示出自然语言的许多属性。在一个简单的参考游戏与两个神经网络为基础的代理，我们分析的对象符号映射，试图了解什么样的战略是用来开发的紧急语言。我们看到，当环境是均匀分布的，代理依赖于一个随机子集的功能来描述对象。当我们修改对象，使一个特征不均匀分布时，代理意识到它的信息量较少，并开始忽略它，令人惊讶的是，它们更好地利用了剩余的特征。这个有趣的结果表明，更自然、分布更不均匀的环境可能有助于刺激行为更好的语言的出现。
英文摘要：
Research in multi-agent cooperation has shown that artificial agents are able to learn to play a simple referential game while developing a shared lexicon. This lexicon is not easy to analyze, as it does not show many properties of a natural language. In a simple referential game with two neural network-based agents, we analyze the object-symbol mapping trying to understand what kind of strategy was used to develop the emergent language. We see that, when the environment is uniformly distributed, the agents rely on a random subset of features to describe the objects. When we modify the objects making one feature non-uniformly distributed,the agents realize it is less informative and start to ignore it, and, surprisingly, they make a better use of the remaining features. This interesting result suggests that more natural, less uniformly distributed environments might aid in spurring the emergence of better-behaved languages.
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。
