Flexibility is one of the hallmarks of human problem‐solving. In everyday life, people adapt to changes in common tasks with little to no additional training. Much of the existing work on flexibility in human problem‐solving has focused on how people adapt to tasks in new domains by drawing on solutions from previously learned domains. In real‐world tasks, however, humans must generalize across a wide range of within‐domain variation. In this work we argue that representational abstraction plays an important role in such within‐domain generalization. We then explore the nature of this representational abstraction in realistically complex tasks like video games by demonstrating how the same model‐based planning framework produces distinct generalization behaviors under different classes of task representation. Finally, we compare the behavior of agents with these task representations to humans in a series of novel grid‐based video game tasks. Our results provide evidence for the claim that within‐domain flexibility in humans derives from task representations composed of propositional rules written in terms of objects and relational categories.

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基于模型的规划中的模型是什么？

灵活性是人类解决问题的标志之一。在日常生活中，人们几乎不需要额外培训就可以适应常见任务的变化。许多关于人类问题解决灵活性的现有工作都集中在人们如何通过利用先前学习领域的解决方案来适应新领域中的任务。然而，在现实世界的任务中，人类必须概括广泛的域内变化。在这项工作中，我们认为表征抽象在这种域内泛化中起着重要作用。然后，我们通过演示相同的基于模型的规划框架如何在不同类别的任务表示下产生不同的泛化行为，来探索视频游戏等现实复杂任务中这种表示抽象的性质。最后，我们在一系列新颖的基于网格的视频游戏任务中将具有这些任务表示的代理的行为与人类进行比较。我们的结果为以下说法提供了证据：人类的域内灵活性源自任务表示，任务表示由根据对象和关系类别编写的命题规则组成。