Many real-world tasks require agents to coordinate their behavior to achieve shared goals. Successful collaboration requires not only adopting the same communicative conventions, but also grounding these conventions in the same task-appropriate conceptual abstractions. We investigate how humans use natural language to collaboratively solve physical assembly problems more effectively over time. Human participants were paired up in an online environment to reconstruct scenes containing two block towers. One participant could see the target towers, and sent assembly instructions for the other participant to reconstruct. Participants provided increasingly concise instructions across repeated attempts on each pair of towers, using higher-level referring expressions that captured each scene's hierarchical structure. To explain these findings, we extend recent probabilistic models of ad-hoc convention formation with an explicit perceptual learning mechanism. These results shed light on the inductive biases that enable intelligent agents to coordinate upon shared procedural abstractions.
翻译:许多现实世界的任务要求代理人协调他们的行为,以实现共同目标。成功的合作不仅需要采用同样的交流公约,而且需要将这些公约建立在与任务相适应的概念抽象概念上。我们调查人类如何使用自然语言在一段时间内更有效地合作解决物理组装问题。人类参与者被放在一个在线环境中,以重建包含两座街区塔的场景。一位与会者可以看到目标塔,并给另一位参与者发出组装指示。与会者在每对塔上反复尝试时提供了越来越简明的指示,使用了更高层次的引用表达方式,捕捉了每一场景的等级结构。为了解释这些结论,我们推广了最近模拟会议形成的各种概率模型,并建立了明确的认知学习机制。这些结果揭示了使智能分子能够协调共同的程序抽象的感官偏见。