Multi-Agent Cooperation and the Emergence of (Natural) Language
Multi-Agent Cooperation and the Emergence of (Natural) Language
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发表时间：
2016-11
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通讯作者：
Angeliki Lazaridou;A. Peysakhovich;Marco Baroni
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作者：
Angeliki Lazaridou;A. Peysakhovich;Marco Baroni
目前训练自然语言系统的主流方法是让它们接触大量的文本。如果我们对开发交互式机器（如会话代理）感兴趣，这种被动学习是有问题的。我们提出了一个基于多智能体交流的语言学习框架。我们在参照游戏的背景下研究这种学习。在这些游戏中，发送者和接收者看到一对图像。发送者被告知其中一个是目标，并被允许从固定的、任意的词汇表向接收者发送消息。接收方必须依靠此消息来识别目标。因此，出于交流的需要，代理们交互式地发展了自己的语言。我们证明了两个具有简单结构的网络能够在参考博弈中学习协调。我们进一步探索如何改变游戏环境，使游戏中产生的“词义”能够更好地反映图像的直观语义属性。此外，我们提出了一种简单的策略，将智能体的代码根植于自然语言中。这两者都是开发能够与人类有效沟通的机器的必要步骤。
The current mainstream approach to train natural language systems is to expose them to large amounts of text. This passive learning is problematic if we are interested in developing interactive machines, such as conversational agents. We propose a framework for language learning that relies on multi-agent communication. We study this learning in the context of referential games. In these games, a sender and a receiver see a pair of images. The sender is told one of them is the target and is allowed to send a message from a fixed, arbitrary vocabulary to the receiver. The receiver must rely on this message to identify the target. Thus, the agents develop their own language interactively out of the need to communicate. We show that two networks with simple configurations are able to learn to coordinate in the referential game. We further explore how to make changes to the game environment to cause the "word meanings" induced in the game to better reflect intuitive semantic properties of the images. In addition, we present a simple strategy for grounding the agents' code into natural language. Both of these are necessary steps towards developing machines that are able to communicate with humans productively.