Towards Evaluating Plan Generation Approaches with Instructional Texts

Understand

Recent research in behaviour understanding through language grounding has shown it is possible to automatically generate behaviour models from textual instructions.

• These models usually have goal-oriented structure and are modelled with different formalisms from the planning domain such as the Planning Domain Definition Language.
• One major problem that still remains is that there are no benchmark datasets for comparing the different model generation approaches, as each approach is usually evaluated on domain-specific application.
• To allow the objective comparison of different methods for model generation from textual instructions, in this report we introduce a dataset consisting of 83 textual instructions in English language, their refinement in a more structured form as well as manually developed plans for each of the instructions.