@InProceedings{yordanova:2017:RANLP1,
  author    = {Yordanova, Kristina},
  title     = {Automatic Generation of Situation Models for Plan Recognition Problems},
  booktitle = {Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017},
  month     = {September},
  year      = {2017},
  address   = {Varna, Bulgaria},
  publisher = {INCOMA Ltd.},
  pages     = {823--830},
  abstract  = {Recent attempts at behaviour understanding through language grounding have
	shown that it is possible to automatically generate models for planning
	problems from textual instructions.
	One drawback of these approaches is that they either do not make use of the
	semantic structure behind the model elements identified in the text, or they
	manually incorporate a collection of concepts with semantic relationships
	between them. 
	We call this collection of knowledge situation model. 
	The situation model introduces additional context information to the model.
	It could also potentially reduce the complexity of the planning problem
	compared to models that do not use situation models.
	To address this problem, we propose an approach that automatically generates
	the situation model from textual instructions. 
	The approach is able to identify various hierarchical, spatial, directional,
	and causal relations. 
	We use the situation model to automatically generate planning problems in a
	PDDL notation and we show that the situation model reduces the complexity of
	the PDDL model in terms of number of operators and branching factor compared to
	planning models that do not make use of situation models.},
  url       = {https://doi.org/10.26615/978-954-452-049-6_105}
}