Framer: Planning Models from Natural Language Action Descriptions

In this paper, we describe an approach for learning planning
domain models directly from natural language (NL) descriptions
of activity sequences. The modelling problem has been
identified as a bottleneck for the widespread exploitation of
various technologies in Artificial Intelligence, including automated
planners. There have been great advances in modelling
assisting and model generation tools, including a wide range
of domain model acquisition tools. However, for modelling
tools, there is the underlying assumption that the user can
formulate the problem using some formal language. And even
in the case of the domain model acquisition tools, there is
still a requirement to specify input plans in an easily machine
readable format. Providing this type of input is impractical for
many potential users. This motivates us to generate planning
domain models directly from NL descriptions, as this would
provide an important step in extending the widespread adoption
of planning techniques. We start from NL descriptions of
actions and use NL analysis to construct structured representations,
from which we construct formal representations of the
action sequences. The generated action sequences provide the
necessary structured input for inducing a PDDL domain, using
domain model acquisition technology. In order to capture
a concise planning model, we use an estimate of functional
similarity, so sentences that describe similar behaviours are
represented by the same planning operator. We validate our
approach with a user study, where participants are tasked with
describing the activities occurring in several videos. Then our
system is used to learn planning domain models using the
participants’ NL input. We demonstrate that our approach is
effective at learning models on these task