  A major component for developing intelligent and autonomous robots is a
suitable knowledge representation, from which a robot can acquire knowledge
about its actions or world. However, unlike humans, robots cannot creatively
adapt to novel scenarios, as their knowledge and environment are rigidly
defined. To address the problem of producing novel and flexible task plans
called task trees, we explore how we can derive plans with concepts not
originally in the robot's knowledge base. Existing knowledge in the form of a
knowledge graph is used as a base of reference to create task trees that are
modified with new object or state combinations. To demonstrate the flexibility
of our method, we randomly selected recipes from the Recipe1M+ dataset and
generated their task trees. The task trees were then thoroughly checked with a
visualization tool that portrays how each ingredient changes with each action
to produce the desired meal. Our results indicate that the proposed method can
produce task plans with high accuracy even for never-before-seen ingredient
combinations.
