A central but unresolved aspect of problem-solving in AI is the capability to introduce and use abstractions, something humans excel at. Work in cognitive science has demonstrated that humans tend towards higher levels of abstraction when engaged in collaborative task-oriented communication, enabling gradually shorter and more information-efficient utterances. Several computational methods have attempted to replicate this phenomenon, but all make unrealistic simplifying assumptions about how abstractions are introduced and learned. Our method, Abstractions for Communicating Efficiently (ACE), overcomes these limitations through a neuro-symbolic approach. On the symbolic side, we draw on work from library learning for proposing abstractions. We combine this with neural methods for communication and reinforcement learning, via a novel use of bandit algorithms for controlling the exploration and exploitation trade-off in introducing new abstractions. ACE exhibits similar tendencies to humans on a collaborative construction task from the cognitive science literature, where one agent (the architect) instructs the other (the builder) to reconstruct a scene of block-buildings. ACE results in the emergence of an efficient language as a by-product of collaborative communication. Beyond providing mechanistic insights into human communication, our work serves as a first step to providing conversational agents with the ability for human-like communicative abstractions.

翻译：人工智能问题解决中一个核心但尚未解决的方面是引入和使用抽象的能力，而这正是人类所擅长的。认知科学的研究表明，人类在进行协作性任务导向的交流时倾向于采用更高层次的抽象，从而实现逐步缩短且信息效率更高的表达。已有多种计算方法尝试复现这一现象，但均对抽象的引入和学习方式做出了不切实际的简化假设。我们的方法——高效通信抽象机制（ACE）——通过神经符号方法克服了这些局限。在符号层面，我们借鉴了库学习的研究来提出抽象表示。我们将其与用于通信和强化学习的神经方法相结合，通过一种新颖的利用多臂赌博机算法来控制引入新抽象时探索与利用的权衡。在一个源自认知科学文献的协作建造任务中（其中一名智能体作为“建筑师”指导另一名“建造者”重建积木场景），ACE表现出与人类相似的倾向性。ACE通过协作交流自然衍生出一种高效的语言。除了为人类交流机制提供理论洞见外，我们的工作还为对话智能体赋予类人通信抽象能力迈出了第一步。