Generalised Domain Model Acquisition from Action Traces

One approach to the problem of formulating domain models
for planning is to learn the models from example action sequences.
The LOCM system demonstrated the feasibility of
learning domain models from example action sequences only,
with no observation of states before, during or after the plans.
LOCM uses an object-centred representation, in which each
object is represented by a single parameterised state machine.
This makes it powerful for learning domains which fit within
that representation, but there are some well-known domains
which do not.
This paper introduces LOCM2, a novel algorithm in which
the domain representation of LOCM is generalised to allow
multiple parameterised state machines to represent a single
object. This extends the coverage of domains for which an
adequate domain model can be learned. The LOCM2 algorithm
is described and evaluated by testing domain learning
from example plans from published results of past International
Planning Competitions.