  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.
