HALMA：类人抽象学习与快速问题解决的可负担性
HALMA: Humanlike Abstraction Learning Meets Affordance in Rapid Problem Solving
摘要：
人类学习组成和因果抽象，\ie，知识，响应自然主义任务的结构。当一个问题解决任务涉及一些对象时，幼儿会首先与这些对象互动，以判断它们是什么以及可以用它们做什么。利用这些概念，他们可以理解这个任务的内部结构，而不必看到所有的问题实例。值得注意的是，他们进一步建立认知上可执行的策略，以解决\emph{rapidly}新问题。为了使学习代理具有类似的能力，我们认为应该有三个层次的泛化代理如何表示其知识：感知，概念和算法。在本文中，我们设计了第一个系统的基准，提供涵盖所有三个级别的联合评估。该基准测试围绕一个新的任务域HALMA，用于视觉概念开发和快速解决问题。独特的是，HALMA有一个最小但完整的概念空间，在此基础上，我们引入了一种新的范式，严格诊断和解剖学习代理的能力，在理解和概括复杂和结构的概念。我们对具有各种归纳偏差的强化学习代理进行了广泛的实验，并仔细报告了他们的熟练程度和弱点。
英文摘要：
Humans learn compositional and causal abstraction, \ie, knowledge, in response to the structure of naturalistic tasks. When presented with a problem-solving task involving some objects, toddlers would first interact with these objects to reckon what they are and what can be done with them. Leveraging these concepts, they could understand the internal structure of this task, without seeing all of the problem instances. Remarkably, they further build cognitively executable strategies to \emph{rapidly} solve novel problems. To empower a learning agent with similar capability, we argue there shall be three levels of generalization in how an agent represents its knowledge: perceptual, conceptual, and algorithmic. In this paper, we devise the very first systematic benchmark that offers joint evaluation covering all three levels. This benchmark is centered around a novel task domain, HALMA, for visual concept development and rapid problem-solving. Uniquely, HALMA has a minimum yet complete concept space, upon which we introduce a novel paradigm to rigorously diagnose and dissect learning agents' capability in understanding and generalizing complex and structural concepts. We conduct extensive experiments on reinforcement learning agents with various inductive biases and carefully report their proficiency and weakness.
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。
