  In the field of Automated Planning and Scheduling (APS), intelligent agents
by virtue require an action model (blueprints of actions whose interleaved
executions effectuates transitions of the system state) in order to plan and
solve real world problems. It is, however, becoming increasingly cumbersome to
codify this model, and is more efficient to learn it from observed plan
execution sequences (training data). While the underlying objective is to
subsequently plan from this learnt model, most approaches fall short as
anything less than a flawless reconstruction of the underlying model renders it
unusable in certain domains. This work presents a novel approach using long
short-term memory (LSTM) techniques for the acquisition of the underlying
action model. We use the sequence labelling capabilities of LSTMs to isolate
from an exhaustive model set a model identical to the one responsible for
producing the training data. This isolation capability renders our approach as
an effective one.
