Symbolic planning is a powerful technique to solve complex tasks that require long sequences of actions and can equip an intelligent agent with complex behavior. The downside of this approach is the necessity for suitable symbolic representations describing the state of the environment as well as the actions that can change it. Traditionally such representations are carefully hand-designed by experts for distinct problem domains, which limits their transferability to different problems and environment complexities. In this paper, we propose a novel concept to generalize symbolic actions using a given entity hierarchy and observed similar behavior. In a simulated grid-based kitchen environment, we show that type-generalized actions can be learned from few observations and generalize to novel situations. Incorporating an additional on-the-fly generalization mechanism during planning, unseen task combinations, involving longer sequences, novel entities and unexpected environment behavior, can be solved.

翻译：符号规划是一种强大的技术，用于解决需要长序列动作的复杂任务，并能赋予智能体复杂的行为。然而，这种方法的一个缺点是需要合适的符号化表示来描述环境状态以及能改变状态的行动。传统上，这类表示由专家针对特定问题领域精心手工设计，这限制了其在不同问题与环境复杂度之间的可迁移性。在本文中，我们提出了一种新概念，利用给定的实体层级结构和观察到的相似行为来泛化符号化动作。在模拟的网格化厨房环境中，我们证明类别泛化动作可以从少量观察中学习，并泛化到新情境。通过引入规划过程中额外的即时泛化机制，可以解决包含更长序列、新实体及意外环境行为的未见任务组合。