Towards Learning Operator Schema from Free Text

In automated planning current research is focused
on developing domain-independent planning engines.
These require domain models, written in a standard
input language such as PDDL to supply knowledge of
the planning application and task, before they can be
used. The main component of a domain model is the
representation of actions in the form of lifted opera-
tor schema. The acquisition and engineering of these
schema is an important area of research, as this process
is recognised as being di�cult and laborious even for
planning experts.
A fruitful line of research is to investigate mechanisms
to automatically learn planning domain models. Re-
cent research has studied learning from structured or
re�ned inputs supplied by a training agent (Cress-
well, McCluskey, and West 2011; Zhuo et al. 2010;
Wu, Yang, and Jiang 2005; McCluskey et al. 2010). An
alternative method would be to allow planning agents
to learn and develop the domain models by observa-
tion. One freely available source for learning actions
is selected web text; here actions are represented as
verbs in natural language. This project aims to in-
vestigate the possibility of extracting formal structures
representing actions from free text. We intend to utilise
large text corpuses available on-line from which to ex-
tract such action knowledge, and learn operator schema
in a formal language that can be converted to PDDL.